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Configuration error
import streamlit as st | |
import pandas as pd | |
import asyncio | |
from datetime import datetime | |
from fake_useragent import UserAgent | |
from bs4 import BeautifulSoup | |
from googlesearch import search | |
import boto3 | |
from botocore.exceptions import NoCredentialsError, PartialCredentialsError, ClientError | |
from openai import OpenAI | |
import logging | |
import json | |
import re | |
import os | |
import sqlite3 | |
import requests | |
from requests.adapters import HTTPAdapter | |
from urllib3.util.retry import Retry | |
from concurrent.futures import ThreadPoolExecutor | |
import time | |
import random | |
import plotly.express as px | |
from dotenv import load_dotenv | |
load_dotenv() # Load environment variables from .env file | |
AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID") | |
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY") | |
REGION_NAME = os.getenv("AWS_REGION_NAME", "us-east-1") | |
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
OPENAI_API_BASE = os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1") | |
openai_model = os.getenv("OPENAI_MODEL", "gpt-3.5-turbo") | |
if not all([AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, OPENAI_API_KEY]): | |
raise ValueError("Missing required environment variables. Please check your .env file.") | |
client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE) | |
# SQLite configuration | |
sqlite_db_path = "autoclient.db" | |
# Initialize AWS SES client | |
try: | |
ses_client = boto3.client('ses', | |
aws_access_key_id=AWS_ACCESS_KEY_ID, | |
aws_secret_access_key=AWS_SECRET_ACCESS_KEY, | |
region_name=REGION_NAME) | |
except (NoCredentialsError, PartialCredentialsError) as e: | |
logging.error(f"AWS SES client initialization failed: {e}") | |
raise | |
# SQLite connection | |
def get_db_connection(): | |
try: | |
conn = sqlite3.connect(sqlite_db_path) | |
conn.row_factory = sqlite3.Row | |
return conn | |
except sqlite3.Error as e: | |
logging.error(f"Database connection error: {e}") | |
st.error(f"Failed to connect to the database. Please try again later.") | |
return None | |
# HTTP session with retry strategy | |
session = requests.Session() | |
retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504]) | |
session.mount('https://', HTTPAdapter(max_retries=retries)) | |
# Setup logging | |
try: | |
logging.basicConfig(level=logging.INFO, filename='app.log', filemode='a', | |
format='%(asctime)s - %(levelname)s - %(message)s') | |
except IOError as e: | |
print(f"Error setting up logging: {e}") | |
raise | |
# Input validation functions | |
def validate_name(name): | |
if not name or not name.strip(): | |
raise ValueError("Name cannot be empty or just whitespace") | |
if len(name) > 100: | |
raise ValueError("Name is too long (max 100 characters)") | |
return name.strip() | |
def validate_email(email): | |
if not re.match(r"[^@]+@[^@]+\.[^@]+", email): | |
raise ValueError("Invalid email address") | |
return email | |
def validate_campaign_type(campaign_type): | |
valid_types = ["Email", "SMS"] | |
if campaign_type not in valid_types: | |
raise ValueError(f"Invalid campaign type. Must be one of {valid_types}") | |
return campaign_type | |
def validate_id(id_value, id_type): | |
try: | |
id_int = int(id_value.split(':')[0] if ':' in str(id_value) else id_value) | |
if id_int <= 0: | |
raise ValueError | |
return id_int | |
except (ValueError, AttributeError): | |
raise ValueError(f"Invalid {id_type} ID") | |
def validate_status(status, valid_statuses): | |
if status not in valid_statuses: | |
raise ValueError(f"Invalid status. Must be one of {valid_statuses}") | |
return status | |
def validate_num_results(num_results): | |
if not isinstance(num_results, int) or num_results < 0: | |
raise ValueError("Invalid number of results") | |
return num_results | |
# Initialize database | |
def initialize_database(): | |
conn = get_db_connection() | |
if not conn: | |
st.error("Failed to initialize database. Please check your database configuration.") | |
return | |
cursor = conn.cursor() | |
cursor.executescript(''' | |
CREATE TABLE IF NOT EXISTS projects ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
project_name TEXT NOT NULL, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS campaigns ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
campaign_name TEXT NOT NULL, | |
project_id INTEGER, | |
campaign_type TEXT NOT NULL, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (project_id) REFERENCES projects (id) | |
); | |
CREATE TABLE IF NOT EXISTS message_templates ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
template_name TEXT NOT NULL, | |
subject TEXT, | |
body_content TEXT NOT NULL, | |
campaign_id INTEGER, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id) | |
); | |
CREATE TABLE IF NOT EXISTS leads ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
email TEXT, | |
phone TEXT, | |
first_name TEXT, | |
last_name TEXT, | |
company TEXT, | |
job_title TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS lead_sources ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
lead_id INTEGER, | |
search_term_id INTEGER, | |
url TEXT, | |
page_title TEXT, | |
meta_description TEXT, | |
http_status INTEGER, | |
scrape_duration TEXT, | |
meta_tags TEXT, | |
phone_numbers TEXT, | |
content TEXT, | |
tags TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (lead_id) REFERENCES leads (id), | |
FOREIGN KEY (search_term_id) REFERENCES search_terms (id) | |
); | |
CREATE TABLE IF NOT EXISTS campaign_leads ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
campaign_id INTEGER, | |
lead_id INTEGER, | |
status TEXT DEFAULT 'active', | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id), | |
FOREIGN KEY (lead_id) REFERENCES leads (id) | |
); | |
CREATE TABLE IF NOT EXISTS messages ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
campaign_id INTEGER, | |
lead_id INTEGER, | |
template_id INTEGER, | |
customized_subject TEXT, | |
customized_content TEXT, | |
sent_at TIMESTAMP, | |
status TEXT DEFAULT 'pending', | |
engagement_data TEXT, | |
message_id TEXT, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id), | |
FOREIGN KEY (lead_id) REFERENCES leads (id), | |
FOREIGN KEY (template_id) REFERENCES message_templates (id) | |
); | |
CREATE TABLE IF NOT EXISTS search_terms ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
term TEXT NOT NULL, | |
campaign_id INTEGER, | |
status TEXT DEFAULT 'pending', | |
processed_leads INTEGER DEFAULT 0, | |
last_processed_at TIMESTAMP, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id) | |
); | |
CREATE TABLE IF NOT EXISTS knowledge_base ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
project_id INTEGER, | |
name TEXT, | |
bio TEXT, | |
values TEXT, | |
contact_name TEXT, | |
contact_role TEXT, | |
contact_email TEXT, | |
company_description TEXT, | |
company_mission TEXT, | |
company_target_market TEXT, | |
company_other TEXT, | |
product_name TEXT, | |
product_description TEXT, | |
product_target_customer TEXT, | |
product_other TEXT, | |
other_context TEXT, | |
example_email TEXT, | |
complete_version TEXT, | |
medium_version TEXT, | |
small_version TEXT, | |
temporal_version TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (project_id) REFERENCES projects (id) | |
); | |
''') | |
conn.commit() | |
conn.close() | |
logging.info("Database initialized successfully!") | |
import streamlit as st | |
import pandas as pd | |
import asyncio | |
from datetime import datetime | |
from fake_useragent import UserAgent | |
from bs4 import BeautifulSoup | |
from googlesearch import search | |
import boto3 | |
from botocore.exceptions import NoCredentialsError, PartialCredentialsError, ClientError | |
import openai | |
import logging | |
import json | |
import re | |
import os | |
import sqlite3 | |
import requests | |
from requests.adapters import HTTPAdapter | |
from urllib3.util.retry import Retry | |
from concurrent.futures import ThreadPoolExecutor | |
import time | |
import random | |
import plotly.express as px | |
AWS_ACCESS_KEY_ID = "AKIASO2XOMEGIVD422N7" | |
AWS_SECRET_ACCESS_KEY = "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" | |
REGION_NAME = "us-east-1" | |
openai.api_key = os.getenv("OPENAI_API_KEY", "sk-1234") | |
openai.api_base = os.getenv("OPENAI_API_BASE", "https://openai-proxy-kl3l.onrender.com") | |
openai_model = "gpt-3.5-turbo" | |
# SQLite configuration | |
sqlite_db_path = "autoclient.db" | |
# Ensure the database file exists | |
try: | |
if not os.path.exists(sqlite_db_path): | |
open(sqlite_db_path, 'w').close() | |
except IOError as e: | |
logging.error(f"Failed to create database file: {e}") | |
raise | |
# Initialize AWS SES client | |
try: | |
ses_client = boto3.client('ses', | |
aws_access_key_id=AWS_ACCESS_KEY_ID, | |
aws_secret_access_key=AWS_SECRET_ACCESS_KEY, | |
region_name=REGION_NAME) | |
except (NoCredentialsError, PartialCredentialsError) as e: | |
logging.error(f"AWS SES client initialization failed: {e}") | |
raise | |
# SQLite connection | |
def get_db_connection(): | |
conn = sqlite3.connect(sqlite_db_path, check_same_thread=False) | |
conn.row_factory = sqlite3.Row | |
return conn | |
# HTTP session with retry strategy | |
session = requests.Session() | |
retries = Retry(total=5, backoff_factor=1, status_forcelist=[502, 503, 504]) | |
session.mount('https://', HTTPAdapter(max_retries=retries)) | |
# Setup logging | |
try: | |
logging.basicConfig(level=logging.INFO, filename='app.log', filemode='a', | |
format='%(asctime)s - %(levelname)s - %(message)s') | |
except IOError as e: | |
print(f"Error setting up logging: {e}") | |
raise | |
# Input validation functions | |
def validate_name(name): | |
if not name or not name.strip(): | |
raise ValueError("Name cannot be empty or just whitespace") | |
if len(name) > 100: | |
raise ValueError("Name is too long (max 100 characters)") | |
return name.strip() | |
def validate_email(email): | |
if not re.match(r"[^@]+@[^@]+\.[^@]+", email): | |
raise ValueError("Invalid email address") | |
return email | |
def validate_campaign_type(campaign_type): | |
valid_types = ["Email", "SMS"] | |
if campaign_type not in valid_types: | |
raise ValueError(f"Invalid campaign type. Must be one of {valid_types}") | |
return campaign_type | |
def validate_id(id_value, id_type): | |
try: | |
id_int = int(id_value.split(':')[0] if ':' in str(id_value) else id_value) | |
if id_int <= 0: | |
raise ValueError | |
return id_int | |
except (ValueError, AttributeError): | |
raise ValueError(f"Invalid {id_type} ID") | |
def validate_status(status, valid_statuses): | |
if status not in valid_statuses: | |
raise ValueError(f"Invalid status. Must be one of {valid_statuses}") | |
return status | |
def validate_num_results(num_results): | |
if not isinstance(num_results, int) or num_results < 0: | |
raise ValueError("Invalid number of results") | |
return num_results | |
# Initialize database | |
def init_db(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.executescript(''' | |
CREATE TABLE IF NOT EXISTS projects ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
project_name TEXT NOT NULL, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS campaigns ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
campaign_name TEXT NOT NULL, | |
project_id INTEGER, | |
campaign_type TEXT NOT NULL, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (project_id) REFERENCES projects (id) | |
); | |
CREATE TABLE IF NOT EXISTS message_templates ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
template_name TEXT NOT NULL, | |
subject TEXT, | |
body_content TEXT NOT NULL, | |
campaign_id INTEGER, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id) | |
); | |
CREATE TABLE IF NOT EXISTS leads ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
email TEXT, | |
phone TEXT, | |
first_name TEXT, | |
last_name TEXT, | |
company TEXT, | |
job_title TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS lead_sources ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
lead_id INTEGER, | |
search_term_id INTEGER, | |
url TEXT, | |
page_title TEXT, | |
meta_description TEXT, | |
http_status INTEGER, | |
scrape_duration TEXT, | |
meta_tags TEXT, | |
phone_numbers TEXT, | |
content TEXT, | |
tags TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (lead_id) REFERENCES leads (id), | |
FOREIGN KEY (search_term_id) REFERENCES search_terms (id) | |
); | |
CREATE TABLE IF NOT EXISTS campaign_leads ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
campaign_id INTEGER, | |
lead_id INTEGER, | |
status TEXT DEFAULT 'active', | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id), | |
FOREIGN KEY (lead_id) REFERENCES leads (id) | |
); | |
CREATE TABLE IF NOT EXISTS messages ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
campaign_id INTEGER, | |
lead_id INTEGER, | |
template_id INTEGER, | |
customized_subject TEXT, | |
customized_content TEXT, | |
sent_at TIMESTAMP, | |
status TEXT DEFAULT 'pending', | |
engagement_data TEXT, | |
message_id TEXT, | |
FOREIGN KEY (campaign_id) REFERENCES campaigns (id), | |
FOREIGN KEY (lead_id) REFERENCES leads (id), | |
FOREIGN KEY (template_id) REFERENCES message_templates (id) | |
); | |
CREATE TABLE IF NOT EXISTS search_term_groups ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
name TEXT NOT NULL, | |
email_template TEXT, | |
description TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS search_terms ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
term TEXT NOT NULL, | |
group_id INTEGER, | |
category TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (group_id) REFERENCES search_term_groups (id) | |
); | |
CREATE TABLE IF NOT EXISTS knowledge_base ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
kb_name TEXT, | |
kb_bio TEXT, | |
kb_values TEXT, | |
contact_name TEXT, | |
contact_role TEXT, | |
contact_email TEXT, | |
company_description TEXT, | |
company_mission TEXT, | |
company_target_market TEXT, | |
company_other TEXT, | |
product_name TEXT, | |
product_description TEXT, | |
product_target_customer TEXT, | |
product_other TEXT, | |
other_context TEXT, | |
example_email TEXT, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS ai_request_logs ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
function_name TEXT NOT NULL, | |
prompt TEXT NOT NULL, | |
response TEXT NOT NULL, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP | |
); | |
CREATE TABLE IF NOT EXISTS optimized_search_terms ( | |
id INTEGER PRIMARY KEY AUTOINCREMENT, | |
term TEXT NOT NULL, | |
original_term_id INTEGER, | |
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, | |
FOREIGN KEY (original_term_id) REFERENCES search_terms (id) | |
); | |
''') | |
conn.commit() | |
conn.close() | |
logging.info("Database initialized successfully!") | |
# Call this at the start of your script | |
init_db() | |
def alter_messages_table(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
try: | |
# Check if the column exists | |
cursor.execute("PRAGMA table_info(messages)") | |
columns = [column[1] for column in cursor.fetchall()] | |
if 'message_id' not in columns: | |
cursor.execute("ALTER TABLE messages ADD COLUMN message_id TEXT") | |
conn.commit() | |
logging.info("Added message_id column to messages table") | |
else: | |
logging.info("message_id column already exists in messages table") | |
except sqlite3.Error as e: | |
logging.error(f"Error altering messages table: {e}") | |
finally: | |
conn.close() | |
alter_messages_table() | |
# Function to create a new project | |
def create_project(project_name): | |
project_name = validate_name(project_name) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("INSERT INTO projects (project_name) VALUES (?)", (project_name,)) | |
project_id = cursor.lastrowid | |
conn.commit() | |
conn.close() | |
return project_id | |
# Function to create a new campaign | |
def create_campaign(campaign_name, project_id, campaign_type): | |
campaign_name = validate_name(campaign_name) | |
project_id = validate_id(project_id, "project") | |
campaign_type = validate_campaign_type(campaign_type) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("INSERT INTO campaigns (campaign_name, project_id, campaign_type) VALUES (?, ?, ?)", | |
(campaign_name, project_id, campaign_type)) | |
campaign_id = cursor.lastrowid | |
conn.commit() | |
conn.close() | |
return campaign_id | |
# Function to create a new message template | |
def create_message_template(template_name, subject, body_content, campaign_id): | |
template_name = validate_name(template_name) | |
subject = validate_name(subject) | |
campaign_id = validate_id(campaign_id, "campaign") | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT INTO message_templates (template_name, subject, body_content, campaign_id) | |
VALUES (?, ?, ?, ?) | |
""", (template_name, subject, body_content, campaign_id)) | |
template_id = cursor.lastrowid | |
conn.commit() | |
conn.close() | |
return template_id | |
def create_session(): | |
session = requests.Session() | |
retries = Retry(total=5, backoff_factor=0.1, status_forcelist=[500, 502, 503, 504]) | |
session.mount('http://', HTTPAdapter(max_retries=retries)) | |
session.mount('https://', HTTPAdapter(max_retries=retries)) | |
return session | |
# Define the function to handle real-time logging | |
def update_logs(logs, log_message, log_container): | |
logs.append(log_message) | |
log_container.text_area("Search Logs", "\n".join(logs), height=300) | |
def log_search_term_effectiveness(term, total_results, valid_leads, irrelevant_leads, blogs_found, directories_found): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT INTO search_term_effectiveness | |
(term, total_results, valid_leads, irrelevant_leads, blogs_found, directories_found) | |
VALUES (?, ?, ?, ?, ?, ?) | |
""", (term, total_results, valid_leads, irrelevant_leads, blogs_found, directories_found)) | |
conn.commit() | |
conn.close() | |
def should_scrape_page(meta_description, title): | |
# No premature filtering. We process even if it's a blog or directory | |
return True # We'll always scrape, but categorize as 'blog' or 'directory' | |
def categorize_page_content(soup, url): | |
title = soup.title.string if soup.title else '' | |
meta_description = soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else '' | |
if 'blog' in title.lower() or 'blog' in meta_description.lower(): | |
return 'blog' | |
elif 'directory' in title.lower() or 'directory' in meta_description.lower(): | |
return 'directory' | |
else: | |
return 'company' | |
def manual_search_wrapper(term, num_results, campaign_id, search_type="All Leads", log_container=None): | |
results = [] | |
logs = [] | |
ua = UserAgent() | |
session = create_session() | |
try: | |
log_message = f"Starting search for term: {term}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
search_urls = list(search(term, num=num_results, stop=num_results, pause=2)) | |
log_message = f"Found {len(search_urls)} URLs" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
term_id = add_search_term(term, campaign_id) | |
if term_id is None: | |
raise ValueError(f"Failed to add or retrieve search term ID for '{term}'") | |
for url in search_urls: | |
try: | |
log_message = f"Processing URL: {url}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
headers = { | |
'User-Agent': ua.random, | |
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', | |
'Accept-Language': 'en-US,en;q=0.5', | |
'Referer': 'https://www.google.com/', | |
'DNT': '1', | |
'Connection': 'keep-alive', | |
'Upgrade-Insecure-Requests': '1', | |
} | |
response = session.get(url, headers=headers, timeout=10) | |
response.raise_for_status() | |
response.encoding = 'utf-8' | |
soup = BeautifulSoup(response.text, 'html.parser') | |
emails = find_emails(response.text) | |
phone_numbers = extract_phone_numbers(response.text) | |
log_message = f"Found {len(emails)} emails and {len(phone_numbers)} phone numbers on {url}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
for email in emails: | |
if is_valid_email(email): | |
lead_id = save_lead(email, None, None, None, None, None) | |
save_lead_source(lead_id, term_id, url, soup.title.string if soup.title else None, | |
soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else None, | |
response.status_code, str(response.elapsed), soup.find_all('meta'), extract_phone_numbers(response.text), soup.get_text(), []) | |
log_message = f"Valid email found: {email}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
results.append([email, url, term]) | |
time.sleep(random.uniform(1, 3)) # Random delay between requests | |
except requests.exceptions.RequestException as e: | |
log_message = f"Error processing {url}: {e}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
except Exception as e: | |
log_message = f"Unexpected error processing {url}: {e}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
if len(results) >= num_results: | |
break | |
# Update the processed_leads count for this search term | |
update_processed_leads_count(term_id, len(results)) | |
time.sleep(random.uniform(30, 60)) # Longer delay between search terms | |
except Exception as e: | |
log_message = f"Error in manual search: {e}" | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
log_message = f"Search completed. Found {len(results)} results." | |
if log_container: | |
update_logs(logs, log_message, log_container) | |
return results[:num_results] | |
# Function to add a new search term | |
def add_search_term(term, campaign_id): | |
conn = None | |
try: | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
# Check if the term already exists for this campaign | |
cursor.execute("SELECT id FROM search_terms WHERE term = ? AND campaign_id = ?", (term, campaign_id)) | |
existing_term = cursor.fetchone() | |
if existing_term: | |
term_id = existing_term['id'] | |
logging.info(f"Search term '{term}' already exists for campaign {campaign_id} with ID: {term_id}") | |
else: | |
cursor.execute(''' | |
INSERT INTO search_terms (term, campaign_id, processed_leads) | |
VALUES (?, ?, 0) | |
''', (term, campaign_id)) | |
term_id = cursor.lastrowid | |
logging.info(f"New search term '{term}' added for campaign {campaign_id} with ID: {term_id}") | |
conn.commit() | |
return term_id | |
except sqlite3.Error as e: | |
logging.error(f"Database error in add_search_term: {e}") | |
return None | |
finally: | |
if conn: | |
conn.close() | |
def manual_search_wrapper(term, num_results, campaign_id, search_type="All Leads"): | |
results = [] | |
ua = UserAgent() | |
session = create_session() | |
try: | |
print(f"Starting search for term: {term}") | |
search_urls = list(search(term, num=num_results, stop=num_results, pause=2)) | |
print(f"Found {len(search_urls)} URLs") | |
# Add the search term to the database and get its ID | |
term_id = add_search_term(term, campaign_id) | |
if term_id is None: | |
raise ValueError(f"Failed to add or retrieve search term ID for '{term}'") | |
for url in search_urls: | |
try: | |
print(f"Processing URL: {url}") | |
headers = { | |
'User-Agent': ua.random, | |
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', | |
'Accept-Language': 'en-US,en;q=0.5', | |
'Referer': 'https://www.google.com/', | |
'DNT': '1', | |
'Connection': 'keep-alive', | |
'Upgrade-Insecure-Requests': '1', | |
} | |
response = session.get(url, headers=headers, timeout=10) | |
response.raise_for_status() | |
response.encoding = 'utf-8' | |
soup = BeautifulSoup(response.text, 'html.parser') | |
emails = find_emails(response.text) | |
for email in emails: | |
if is_valid_email(email): | |
lead_id = save_lead(email, None, None, None, None, None) | |
save_lead_source(lead_id, term_id, url, soup.title.string if soup.title else None, | |
soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else None, | |
response.status_code, str(response.elapsed), soup.find_all('meta'), extract_phone_numbers(response.text), soup.get_text(), []) | |
results.append([email, url, term]) | |
time.sleep(random.uniform(1, 3)) # Random delay between requests | |
except requests.exceptions.RequestException as e: | |
print(f"Error processing {url}: {e}") | |
except Exception as e: | |
print(f"Unexpected error processing {url}: {e}") | |
if len(results) >= num_results: | |
break | |
# Update the processed_leads count for this search term | |
update_processed_leads_count(term_id, len(results)) | |
time.sleep(random.uniform(30, 60)) # Longer delay between search terms | |
except Exception as e: | |
print(f"Error in manual search: {e}") | |
print(f"Search completed. Found {len(results)} results.") | |
return results[:num_results] | |
def update_processed_leads_count(term_id, count): | |
conn = None | |
try: | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
UPDATE search_terms | |
SET processed_leads = processed_leads + ? | |
WHERE id = ? | |
''', (count, term_id)) | |
conn.commit() | |
except sqlite3.Error as e: | |
logging.error(f"Database error in update_processed_leads_count: {e}") | |
finally: | |
if conn: | |
conn.close() | |
def update_processed_leads_count(term_id, count): | |
conn = None | |
try: | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
UPDATE search_terms | |
SET processed_leads = processed_leads + ? | |
WHERE id = ? | |
''', (count, term_id)) | |
conn.commit() | |
except sqlite3.Error as e: | |
logging.error(f"Database error in update_processed_leads_count: {e}") | |
finally: | |
if conn: | |
conn.close() | |
# Function to fetch search terms | |
def fetch_search_terms(campaign_id=None): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
if campaign_id: | |
campaign_id = validate_id(campaign_id, "campaign") | |
query = ''' | |
SELECT st.id, st.term, COUNT(DISTINCT ls.lead_id) as processed_leads, st.status | |
FROM search_terms st | |
LEFT JOIN lead_sources ls ON st.id = ls.search_term_id | |
WHERE st.campaign_id = ? | |
GROUP BY st.id | |
''' | |
cursor.execute(query, (campaign_id,)) | |
else: | |
query = ''' | |
SELECT st.id, st.term, COUNT(DISTINCT ls.lead_id) as processed_leads, st.status | |
FROM search_terms st | |
LEFT JOIN lead_sources ls ON st.id = ls.search_term_id | |
GROUP BY st.id | |
''' | |
cursor.execute(query) | |
rows = cursor.fetchall() | |
conn.close() | |
return pd.DataFrame(rows, columns=["ID", "Search Term", "Leads Fetched", "Status"]) | |
# Function to update search term status | |
def update_search_term_status(search_term_id, new_status, processed_leads=None): | |
search_term_id = validate_id(search_term_id, "search term") | |
new_status = validate_status(new_status, ["pending", "completed"]) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
if processed_leads is not None: | |
processed_leads = validate_num_results(processed_leads) | |
cursor.execute(""" | |
UPDATE search_terms | |
SET status = ?, processed_leads = ?, last_processed_at = CURRENT_TIMESTAMP | |
WHERE id = ? | |
""", (new_status, processed_leads, search_term_id)) | |
else: | |
cursor.execute(""" | |
UPDATE search_terms | |
SET status = ?, last_processed_at = CURRENT_TIMESTAMP | |
WHERE id = ? | |
""", (new_status, search_term_id)) | |
conn.commit() | |
conn.close() | |
# Update the save_lead function | |
def save_lead(email, phone, first_name, last_name, company, job_title): | |
email = validate_email(email) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
# Check if the email already exists | |
cursor.execute("SELECT id FROM leads WHERE email = ?", (email,)) | |
existing_lead = cursor.fetchone() | |
if existing_lead: | |
lead_id = existing_lead['id'] | |
logging.info(f"Existing lead found for email {email} with ID {lead_id}") | |
# Update existing lead information if provided | |
if any([phone, first_name, last_name, company, job_title]): | |
cursor.execute(""" | |
UPDATE leads | |
SET phone = COALESCE(?, phone), | |
first_name = COALESCE(?, first_name), | |
last_name = COALESCE(?, last_name), | |
company = COALESCE(?, company), | |
job_title = COALESCE(?, job_title) | |
WHERE id = ? | |
""", (phone, first_name, last_name, company, job_title, lead_id)) | |
logging.info(f"Updated existing lead with ID {lead_id}") | |
else: | |
cursor.execute(""" | |
INSERT INTO leads (email, phone, first_name, last_name, company, job_title) | |
VALUES (?, ?, ?, ?, ?, ?) | |
""", (email, phone, first_name, last_name, company, job_title)) | |
lead_id = cursor.lastrowid | |
logging.info(f"New lead created for email {email} with ID {lead_id}") | |
conn.commit() | |
conn.close() | |
return lead_id | |
def save_lead_source(lead_id, term_id, url, page_title, meta_description, http_status, scrape_duration, meta_tags, phone_numbers, content, tags): | |
conn = None | |
try: | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
INSERT INTO lead_sources (lead_id, search_term_id, url, page_title, meta_description, http_status, scrape_duration, meta_tags, phone_numbers, content, tags) | |
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) | |
''', (lead_id, term_id, url, page_title, meta_description, http_status, scrape_duration, json.dumps(meta_tags), json.dumps(phone_numbers), content, json.dumps(tags))) | |
conn.commit() | |
except sqlite3.Error as e: | |
logging.error(f"Database error in save_lead_source: {e}") | |
finally: | |
if conn: | |
conn.close() | |
# Function to add a lead to a campaign | |
def add_lead_to_campaign(campaign_id, lead_id): | |
campaign_id = validate_id(campaign_id, "campaign") | |
lead_id = validate_id(lead_id, "lead") | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("INSERT OR IGNORE INTO campaign_leads (campaign_id, lead_id) VALUES (?, ?)", | |
(campaign_id, lead_id)) | |
conn.commit() | |
conn.close() | |
# Function to create a new message | |
def create_message(campaign_id, lead_id, template_id, customized_subject, customized_content): | |
campaign_id = validate_id(campaign_id, "campaign") | |
lead_id = validate_id(lead_id, "lead") | |
template_id = validate_id(template_id, "template") | |
customized_subject = validate_name(customized_subject) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT INTO messages (campaign_id, lead_id, template_id, customized_subject, customized_content) | |
VALUES (?, ?, ?, ?, ?) | |
""", (campaign_id, lead_id, template_id, customized_subject, customized_content)) | |
message_id = cursor.lastrowid | |
conn.commit() | |
conn.close() | |
return message_id | |
# Function to update message status | |
def update_message_status(message_id, status, sent_at=None): | |
message_id = validate_id(message_id, "message") | |
status = validate_status(status, ["pending", "sent", "failed"]) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
if sent_at: | |
cursor.execute("UPDATE messages SET status = ?, sent_at = ? WHERE id = ?", | |
(status, sent_at, message_id)) | |
else: | |
cursor.execute("UPDATE messages SET status = ? WHERE id = ?", | |
(status, message_id)) | |
conn.commit() | |
conn.close() | |
# Function to fetch message templates | |
def fetch_message_templates(campaign_id=None): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
if campaign_id: | |
campaign_id = validate_id(campaign_id, "campaign") | |
cursor.execute('SELECT id, template_name FROM message_templates WHERE campaign_id = ?', (campaign_id,)) | |
else: | |
cursor.execute('SELECT id, template_name FROM message_templates') | |
rows = cursor.fetchall() | |
conn.close() | |
return [f"{row['id']}: {row['template_name']}" for row in rows] | |
# Function to fetch projects | |
def fetch_projects(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT id, project_name FROM projects') | |
rows = cursor.fetchall() | |
conn.close() | |
return [f"{row['id']}: {row['project_name']}" for row in rows] | |
# Function to fetch campaigns | |
def fetch_campaigns(): | |
conn = None | |
try: | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT id, campaign_name FROM campaigns ORDER BY campaign_name') | |
campaigns = cursor.fetchall() | |
return [f"{campaign['id']}: {campaign['campaign_name']}" for campaign in campaigns] | |
except sqlite3.Error as e: | |
logging.error(f"Database error in fetch_campaigns: {e}") | |
return [] | |
finally: | |
if conn: | |
conn.close() | |
# Updated bulk search function | |
async def bulk_search(num_results): | |
total_leads = 0 | |
all_results = [] | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT id, term FROM search_terms') | |
search_terms = cursor.fetchall() | |
conn.close() | |
logs = [] | |
for term_id, term in search_terms: | |
leads_found = 0 | |
try: | |
search_urls = list(search(term['term'], num=num_results, stop=num_results, pause=2)) | |
for url in search_urls: | |
if leads_found >= num_results: | |
break | |
try: | |
response = session.get(url, timeout=10) | |
response.encoding = 'utf-8' | |
soup = BeautifulSoup(response.text, 'html.parser') | |
emails = find_emails(response.text) | |
for email in emails: | |
if is_valid_email(email): | |
lead_id = save_lead(email, None, None, None, None, None) | |
save_lead_source(lead_id, term_id, url, soup.title.string if soup.title else None, | |
soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else None, | |
response.status_code, str(response.elapsed), soup.find_all('meta'), extract_phone_numbers(response.text), soup.get_text(), []) | |
all_results.append([email, url, term['term']]) | |
leads_found += 1 | |
total_leads += 1 | |
if leads_found >= num_results: | |
break | |
except Exception as e: | |
logging.error(f"Error processing {url}: {e}") | |
logs.append(f"Processed {leads_found} leads for term '{term['term']}'") | |
except Exception as e: | |
logging.error(f"Error performing search for term '{term['term']}': {e}") | |
update_search_term_status(term_id, 'completed', leads_found) | |
logs.append(f"Bulk search completed. Total new leads found: {total_leads}") | |
return logs, all_results | |
# Function to get email preview | |
def get_email_preview(template_id, from_email, reply_to): | |
template_id = validate_id(template_id, "template") | |
from_email = validate_email(from_email) | |
reply_to = validate_email(reply_to) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT subject, body_content FROM message_templates WHERE id = ?', (template_id,)) | |
template = cursor.fetchone() | |
conn.close() | |
if template: | |
subject, body_content = template | |
preview = f""" | |
<h3>Email Preview</h3> | |
<strong>Subject:</strong> {subject}<br> | |
<strong>From:</strong> {from_email}<br> | |
<strong>Reply-To:</strong> {reply_to}<br> | |
<hr> | |
<h4>Body:</h4> | |
<iframe srcdoc="{body_content.replace('"', '"')}" width="100%" height="600" style="border: 1px solid #ccc;"></iframe> | |
""" | |
return preview | |
else: | |
return "<p>Template not found</p>" | |
# Update the fetch_sent_messages function | |
def fetch_sent_messages(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
SELECT m.id, m.sent_at, l.email, mt.template_name, m.customized_subject, m.customized_content, m.status, m.message_id | |
FROM messages m | |
JOIN leads l ON m.lead_id = l.id | |
JOIN message_templates mt ON m.template_id = mt.id | |
ORDER BY m.sent_at DESC | |
''') | |
messages = cursor.fetchall() | |
conn.close() | |
return pd.DataFrame(messages, columns=['ID', 'Sent At', 'Email', 'Template', 'Subject', 'Content', 'Status', 'Message ID']) | |
# Update the view_sent_messages function | |
def view_sent_messages(): | |
st.header("View Sent Messages") | |
if st.button("Refresh Sent Messages"): | |
st.session_state.sent_messages = fetch_sent_messages() | |
if 'sent_messages' not in st.session_state: | |
st.session_state.sent_messages = fetch_sent_messages() | |
# Display messages in a more organized manner | |
for _, row in st.session_state.sent_messages.iterrows(): | |
with st.expander(f"Message to {row['Email']} - {row['Sent At']}"): | |
st.write(f"**Subject:** {row['Subject']}") | |
st.write(f"**Template:** {row['Template']}") | |
st.write(f"**Status:** {row['Status']}") | |
st.write(f"**Message ID:** {row['Message ID']}") | |
st.write("**Content:**") | |
st.markdown(row['Content'], unsafe_allow_html=True) | |
# Display summary statistics | |
st.subheader("Summary Statistics") | |
total_messages = len(st.session_state.sent_messages) | |
sent_messages = len(st.session_state.sent_messages[st.session_state.sent_messages['Status'] == 'sent']) | |
failed_messages = len(st.session_state.sent_messages[st.session_state.sent_messages['Status'] == 'failed']) | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Messages", total_messages) | |
col2.metric("Sent Messages", sent_messages) | |
col3.metric("Failed Messages", failed_messages) | |
# Update the bulk_send function to be a coroutine instead of an async generator | |
async def bulk_send(template_id, from_email, reply_to, leads): | |
template_id = validate_id(template_id, "template") | |
from_email = validate_email(from_email) | |
reply_to = validate_email(reply_to) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
# Fetch the template | |
cursor.execute('SELECT template_name, subject, body_content FROM message_templates WHERE id = ?', (template_id,)) | |
template = cursor.fetchone() | |
if not template: | |
conn.close() | |
return "Template not found" | |
template_name, subject, body_content = template | |
total_leads = len(leads) | |
logs = [ | |
f"Preparing to send emails to {total_leads} leads", | |
f"Template Name: {template_name}", | |
f"Template ID: {template_id}", | |
f"Subject: {subject}", | |
f"From: {from_email}", | |
f"Reply-To: {reply_to}", | |
"---" | |
] | |
if total_leads == 0: | |
logs.append("No leads found to send emails to. Please check if there are leads in the database and if they have already been sent this template.") | |
return logs | |
total_sent = 0 | |
for index, (lead_id, email) in enumerate(leads, 1): | |
try: | |
response = ses_client.send_email( | |
Source=from_email, | |
Destination={ | |
'ToAddresses': [email], | |
}, | |
Message={ | |
'Subject': { | |
'Data': subject, | |
'Charset': 'UTF-8' | |
}, | |
'Body': { | |
'Html': { | |
'Data': body_content, | |
'Charset': 'UTF-8' | |
} | |
} | |
}, | |
ReplyToAddresses=[reply_to] | |
) | |
message_id = response['MessageId'] | |
save_message(lead_id, template_id, 'sent', datetime.now(), subject, message_id, body_content) | |
total_sent += 1 | |
logs.append(f"[{index}/{total_leads}] Sent email to {email} - Subject: {subject} - MessageId: {message_id}") | |
except ClientError as e: | |
error_code = e.response['Error']['Code'] | |
error_message = e.response['Error']['Message'] | |
logging.error(f"Failed to send email to {email}: {error_code} - {error_message}") | |
save_message(lead_id, template_id, 'failed', None, subject) | |
logs.append(f"[{index}/{total_leads}] Failed to send email to {email}: {error_code} - {error_message}") | |
except Exception as e: | |
logging.error(f"Unexpected error sending email to {email}: {str(e)}") | |
save_message(lead_id, template_id, 'failed', None, subject) | |
logs.append(f"[{index}/{total_leads}] Unexpected error sending email to {email}: {str(e)}") | |
await asyncio.sleep(0.1) # Small delay to allow UI updates | |
logs.append("---") | |
logs.append(f"Bulk send completed. Total emails sent: {total_sent}/{total_leads}") | |
return logs | |
# Update the bulk_send_page function | |
def bulk_send_page(): | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found. Please create a template first.") | |
return | |
with st.form(key="bulk_send_form"): | |
template_id = st.selectbox("Select Message Template", options=templates) | |
from_email = st.text_input("From Email", value="Sami Halawa <[email protected]>") | |
reply_to = st.text_input("Reply To", value="[email protected]") | |
send_option = st.radio("Send to:", | |
["All Leads", | |
"All Not Contacted with this Template", | |
"All Not Contacted with Templates from this Campaign", | |
"Selected Search Term Groups"]) | |
if send_option == "Selected Search Term Groups": | |
groups = fetch_search_term_groups() | |
selected_groups = st.multiselect("Select Search Term Groups", options=groups) | |
group_leads = get_leads_count_for_groups(selected_groups) | |
st.write("Leads in selected groups:") | |
for group, count in group_leads.items(): | |
st.write(f"{group}: {count} leads") | |
filter_option = st.radio("Filter:", | |
["Not Filter Out Leads", | |
"Filter Out blog-directory"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
preview_button = st.form_submit_button(label="Preview Email") | |
with col2: | |
send_button = st.form_submit_button(label="Start Bulk Send") | |
if preview_button: | |
preview = get_email_preview(template_id.split(":")[0], from_email, reply_to) | |
st.components.v1.html(preview, height=600, scrolling=True) | |
if send_button: | |
if send_option == "Selected Search Term Groups" and not selected_groups: | |
st.error("Please select at least one search term group.") | |
return | |
st.session_state.bulk_send_started = True | |
st.session_state.bulk_send_logs = [] | |
st.session_state.bulk_send_progress = 0 | |
leads_to_send = fetch_leads_for_bulk_send(template_id.split(":")[0], send_option, filter_option, selected_groups if send_option == "Selected Search Term Groups" else None) | |
st.write(f"Preparing to send emails to {len(leads_to_send)} leads") | |
bulk_send_coroutine = bulk_send(template_id.split(":")[0], from_email, reply_to, leads_to_send) | |
logs = asyncio.run(bulk_send_coroutine) | |
for log in logs: | |
st.write(log) | |
st.success(f"Bulk send completed. Sent {len(leads_to_send)} emails.") | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found. Please create a template first.") | |
return | |
with st.form(key="bulk_send_form"): | |
template_id = st.selectbox("Select Message Template", options=templates) | |
from_email = st.text_input("From Email", value="Sami Halawa <[email protected]>") | |
reply_to = st.text_input("Reply To", value="[email protected]") | |
send_option = st.radio("Send to:", | |
["All Leads", | |
"All Not Contacted with this Template", | |
"All Not Contacted with Templates from this Campaign", | |
"Selected Search Term Groups"]) | |
if send_option == "Selected Search Term Groups": | |
groups = fetch_search_term_groups() | |
selected_groups = st.multiselect("Select Search Term Groups", options=groups) | |
group_leads = get_leads_count_for_groups(selected_groups) | |
st.write("Leads in selected groups:") | |
for group, count in group_leads.items(): | |
st.write(f"{group}: {count} leads") | |
filter_option = st.radio("Filter:", | |
["Not Filter Out Leads", | |
"Filter Out blog-directory"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
preview_button = st.form_submit_button(label="Preview Email") | |
with col2: | |
send_button = st.form_submit_button(label="Start Bulk Send") | |
if preview_button: | |
preview = get_email_preview(template_id.split(":")[0], from_email, reply_to) | |
st.components.v1.html(preview, height=600, scrolling=True) | |
if send_button: | |
if send_option == "Selected Search Term Groups" and not selected_groups: | |
st.error("Please select at least one search term group.") | |
return | |
st.session_state.bulk_send_started = True | |
st.session_state.bulk_send_logs = [] | |
st.session_state.bulk_send_progress = 0 | |
leads_to_send = fetch_leads_for_bulk_send(template_id.split(":")[0], send_option, filter_option, selected_groups if send_option == "Selected Search Term Groups" else None) | |
st.write(f"Preparing to send emails to {len(leads_to_send)} leads") | |
bulk_send_coroutine = bulk_send(template_id.split(":")[0], from_email, reply_to, leads_to_send) | |
logs = asyncio.run(bulk_send_coroutine) | |
for log in logs: | |
st.write(log) | |
st.success(f"Bulk send completed. Sent {len(leads_to_send)} emails.") | |
st.header("Bulk Send") | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found. Please create a template first.") | |
return | |
with st.form(key="bulk_send_form"): | |
template_id = st.selectbox("Select Message Template", options=templates) | |
from_email = st.text_input("From Email", value="Sami Halawa <[email protected]>") | |
reply_to = st.text_input("Reply To", value="[email protected]") | |
send_option = st.radio("Send to:", | |
["All Leads", | |
"All Not Contacted with this Template", | |
"All Not Contacted with Templates from this Campaign", | |
"Selected Search Term Groups"]) | |
if send_option == "Selected Search Term Groups": | |
groups = fetch_search_term_groups() | |
selected_groups = st.multiselect("Select Search Term Groups", options=groups) | |
group_leads = get_leads_count_for_groups(selected_groups) | |
st.write("Leads in selected groups:") | |
for group, count in group_leads.items(): | |
st.write(f"{group}: {count} leads") | |
filter_option = st.radio("Filter:", | |
["Not Filter Out Leads", | |
"Filter Out blog-directory"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
preview_button = st.form_submit_button(label="Preview Email") | |
with col2: | |
send_button = st.form_submit_button(label="Start Bulk Send") | |
if preview_button: | |
preview = get_email_preview(template_id.split(":")[0], from_email, reply_to) | |
st.components.v1.html(preview, height=600, scrolling=True) | |
if send_button: | |
if send_option == "Selected Search Term Groups" and not selected_groups: | |
st.error("Please select at least one search term group.") | |
return | |
st.session_state.bulk_send_started = True | |
st.session_state.bulk_send_logs = [] | |
st.session_state.bulk_send_progress = 0 | |
# Fetch leads based on send_option and filter_option | |
leads_to_send = fetch_leads_for_bulk_send(template_id.split(":")[0], send_option, filter_option, selected_groups if send_option == "Selected Search Term Groups" else None) | |
st.write(f"Preparing to send emails to {len(leads_to_send)} leads") | |
# Perform bulk send | |
bulk_send_coroutine = bulk_send(template_id.split(":")[0], from_email, reply_to, leads_to_send) | |
logs = asyncio.run(bulk_send_coroutine) | |
# Display logs and statistics | |
for log in logs: | |
st.write(log) | |
st.success(f"Bulk send completed. Sent {len(leads_to_send)} emails.") | |
# New function to fetch search term groups | |
def fetch_search_term_groups(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT id, name FROM search_term_groups") | |
groups = [f"{row['id']}: {row['name']}" for row in cursor.fetchall()] | |
conn.close() | |
return groups | |
# New function to get leads count for groups | |
def get_leads_count_for_groups(selected_groups): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
group_leads = {} | |
for group in selected_groups: | |
group_id = group.split(":")[0] | |
cursor.execute(""" | |
SELECT COUNT(DISTINCT l.id) | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
JOIN search_terms st ON ls.search_term_id = st.id | |
WHERE st.group_id = ? | |
""", (group_id,)) | |
count = cursor.fetchone()[0] | |
group_leads[group] = count | |
conn.close() | |
return group_leads | |
def fetch_leads_for_bulk_send(template_id, send_option, filter_option, selected_groups=None): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
query = """ | |
SELECT DISTINCT l.id, l.email | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
""" | |
if send_option == "All Not Contacted with this Template": | |
query += f""" | |
LEFT JOIN messages m ON l.id = m.lead_id AND m.template_id = {template_id} | |
WHERE m.id IS NULL | |
""" | |
elif send_option == "All Not Contacted with Templates from this Campaign": | |
query += f""" | |
LEFT JOIN messages m ON l.id = m.lead_id | |
LEFT JOIN message_templates mt ON m.template_id = mt.id | |
WHERE m.id IS NULL OR mt.campaign_id != (SELECT campaign_id FROM message_templates WHERE id = {template_id}) | |
""" | |
elif send_option == "Selected Search Term Groups": | |
group_ids = [group.split(":")[0] for group in selected_groups] | |
query += f""" | |
JOIN search_terms st ON ls.search_term_id = st.id | |
WHERE st.group_id IN ({','.join(group_ids)}) | |
""" | |
if filter_option == "Filter Out blog-directory": | |
query += " AND NOT ls.tags LIKE '%blog-directory%'" | |
cursor.execute(query) | |
leads = cursor.fetchall() | |
conn.close() | |
return leads | |
def fetch_leads_for_bulk_send(template_id, send_option, filter_option): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
query = """ | |
SELECT DISTINCT l.id, l.email | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
""" | |
if send_option == "All Not Contacted with this Template": | |
query += f""" | |
LEFT JOIN messages m ON l.id = m.lead_id AND m.template_id = {template_id} | |
WHERE m.id IS NULL | |
""" | |
elif send_option == "All Not Contacted with Templates from this Campaign": | |
query += f""" | |
LEFT JOIN messages m ON l.id = m.lead_id | |
LEFT JOIN message_templates mt ON m.template_id = mt.id | |
WHERE m.id IS NULL OR mt.campaign_id != (SELECT campaign_id FROM message_templates WHERE id = {template_id}) | |
""" | |
if filter_option == "Filter Out blog-directory": | |
query += " AND NOT ls.tags LIKE '%blog-directory%'" | |
cursor.execute(query) | |
leads = cursor.fetchall() | |
conn.close() | |
return leads | |
# Update the save_message function to include the subject | |
def save_message(lead_id, template_id, status, sent_at=None, subject=None, message_id=None, customized_content=None): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
if sent_at: | |
cursor.execute(""" | |
INSERT INTO messages (lead_id, template_id, status, sent_at, customized_subject, message_id, customized_content) | |
VALUES (?, ?, ?, ?, ?, ?, ?) | |
""", (lead_id, template_id, status, sent_at, subject, message_id, customized_content)) | |
else: | |
cursor.execute(""" | |
INSERT INTO messages (lead_id, template_id, status, customized_subject, message_id, customized_content) | |
VALUES (?, ?, ?, ?, ?, ?) | |
""", (lead_id, template_id, status, subject, message_id, customized_content)) | |
conn.commit() | |
conn.close() | |
# Function to sanitize HTML content | |
def sanitize_html(content): | |
return re.sub('<[^<]+?>', '', content) | |
# Function to find valid emails in HTML text | |
def find_emails(html_text): | |
email_regex = re.compile(r'\b[A-Za-z0-9._%+-]+@[^@]+\.[A-Z|a-z]{2,7}\b') | |
all_emails = set(email_regex.findall(html_text)) | |
valid_emails = {email for email in all_emails if is_valid_email(email)} | |
unique_emails = {} | |
for email in valid_emails: | |
domain = email.split('@')[1] | |
if domain not in unique_emails: | |
unique_emails[domain] = email | |
return set(unique_emails.values()) | |
# Function to validate email address | |
def is_valid_email(email): | |
invalid_patterns = [ | |
r'\.png', r'\.jpg', r'\.jpeg', r'\.gif', r'\.bmp', r'^no-reply@', | |
r'^prueba@', r'^\d+[a-z]*@' | |
] | |
typo_domains = ["gmil.com", "gmal.com", "gmaill.com", "gnail.com"] | |
if len(email) < 6 or len(email) > 254: | |
return False | |
for pattern in invalid_patterns: | |
if re.search(pattern, email, re.IGNORECASE): | |
return False | |
domain = email.split('@')[1] | |
if domain in typo_domains or not re.match(r"^[A-Za-z0-9.-]+\.[A-Za-z]{2,}$", domain): | |
return False | |
return True | |
# Function to refresh search terms | |
def refresh_search_terms(campaign_id): | |
return df_to_list(fetch_search_terms(campaign_id)) | |
# Function to convert DataFrame to list of lists | |
def df_to_list(df): | |
return df.values.tolist() | |
# Add this function before the Gradio interface definition | |
import re | |
from urllib.parse import urlparse | |
def extract_phone_numbers(text): | |
phone_pattern = re.compile(r'\b(?:\+?34)?[\s.-]?[6789]\d{2}[\s.-]?\d{3}[\s.-]?\d{3}\b') | |
return phone_pattern.findall(text) | |
def is_probable_blog_or_directory(soup, email): | |
# Check if the email is inside an <article> or <main> tag | |
article_content = soup.find('article') | |
main_content = soup.find('main') | |
if article_content and email in article_content.get_text(): | |
return True | |
if main_content and email in main_content.get_text(): | |
return True | |
# Check if there are multiple emails on the page | |
all_emails = find_emails(soup.get_text()) | |
if len(all_emails) > 3: # Arbitrary threshold, adjust as needed | |
return True | |
# Check for common blog/directory indicators in the URL or title | |
url = soup.find('meta', property='og:url') | |
url = url['content'] if url else '' | |
title = soup.title.string if soup.title else '' | |
indicators = ['blog', 'article', 'news', 'directory', 'list', 'index'] | |
if any(indicator in url.lower() or indicator in title.lower() for indicator in indicators): | |
return True | |
return False | |
def extract_visible_text(soup): | |
# kill all script and style elements | |
for script in soup(["script", "style"]): | |
script.decompose() # rip it out | |
# get text | |
text = soup.get_text() | |
# break into lines and remove leading and trailing space on each | |
lines = (line.strip() for line in text.splitlines()) | |
# break multi-headlines into a line each | |
chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) | |
# drop blank lines | |
text = '\n'.join(chunk for chunk in chunks if chunk) | |
return text | |
def manual_search_wrapper(term, num_results, campaign_id, search_type="All Leads"): | |
results = [] | |
try: | |
print(f"Starting search for term: {term}") | |
search_urls = list(search(term, num=num_results, stop=num_results, pause=2)) | |
print(f"Found {len(search_urls)} URLs") | |
for url in search_urls: | |
try: | |
print(f"Processing URL: {url}") | |
response = session.get(url, timeout=10) | |
response.encoding = 'utf-8' | |
soup = BeautifulSoup(response.text, 'html.parser') | |
title = soup.title.string if soup.title else "" | |
meta_description = soup.find('meta', attrs={'name': 'description'}) | |
meta_description = meta_description['content'] if meta_description else "" | |
emails = find_emails(response.text) | |
phone_numbers = extract_phone_numbers(response.text) | |
meta_tags = [str(tag) for tag in soup.find_all('meta')] | |
content = extract_visible_text(soup) | |
print(f"Found {len(emails)} emails and {len(phone_numbers)} phone numbers on {url}") | |
for email in emails: | |
if is_valid_email(email): | |
tags = [] | |
if is_probable_blog_or_directory(soup, email): | |
tags.append("blog-directory") | |
else: | |
tags.append("company") | |
if search_type == "All Leads" or (search_type == "Exclude Probable Blogs/Directories" and "blog-directory" not in tags): | |
lead_id = save_lead(email, phone_numbers[0] if phone_numbers else None, None, None, None, None) | |
save_lead_source(lead_id, campaign_id, url, title, meta_description, response.status_code, | |
str(response.elapsed), meta_tags, phone_numbers, content, tags) | |
results.append([email, url, title, meta_description, tags]) | |
print(f"Valid email found: {email}") | |
if len(results) >= num_results: | |
break | |
except Exception as e: | |
logging.error(f"Error processing {url}: {e}") | |
print(f"Error processing {url}: {e}") | |
except Exception as e: | |
logging.error(f"Error in manual search: {e}") | |
print(f"Error in manual search: {e}") | |
print(f"Search completed. Found {len(results)} results.") | |
return results[:num_results] | |
# Update save_lead function to be more resilient | |
def save_lead(email, phone, first_name, last_name, company, job_title): | |
try: | |
email = validate_email(email) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT id FROM leads WHERE email = ?", (email,)) | |
existing_lead = cursor.fetchone() | |
if existing_lead: | |
lead_id = existing_lead['id'] | |
print(f"Existing lead found for email {email} with ID {lead_id}") | |
cursor.execute(""" | |
UPDATE leads | |
SET phone = COALESCE(?, phone), | |
first_name = COALESCE(?, first_name), | |
last_name = COALESCE(?, last_name), | |
company = COALESCE(?, company), | |
job_title = COALESCE(?, job_title) | |
WHERE id = ? | |
""", (phone, first_name, last_name, company, job_title, lead_id)) | |
print(f"Updated existing lead with ID {lead_id}") | |
else: | |
cursor.execute(""" | |
INSERT INTO leads (email, phone, first_name, last_name, company, job_title) | |
VALUES (?, ?, ?, ?, ?, ?) | |
""", (email, phone, first_name, last_name, company, job_title)) | |
lead_id = cursor.lastrowid | |
print(f"New lead created for email {email} with ID {lead_id}") | |
conn.commit() | |
conn.close() | |
return lead_id | |
except Exception as e: | |
print(f"Error saving lead: {e}") | |
return None | |
# Update save_lead_source function to be more resilient | |
def save_lead_source(lead_id, search_term_id, url, page_title, meta_description, http_status, scrape_duration, meta_tags, phone_numbers, content, tags): | |
try: | |
if lead_id is None: | |
print("Cannot save lead source: lead_id is None") | |
return | |
lead_id = validate_id(lead_id, "lead") | |
search_term_id = validate_id(search_term_id, "search term") | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT INTO lead_sources (lead_id, search_term_id, url, page_title, meta_description, http_status, scrape_duration, meta_tags, phone_numbers, content, tags) | |
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) | |
""", (lead_id, search_term_id, url, page_title, meta_description, http_status, scrape_duration, json.dumps(meta_tags), json.dumps(phone_numbers), content, json.dumps(tags))) | |
conn.commit() | |
conn.close() | |
print(f"Lead source saved for lead ID {lead_id}") | |
except Exception as e: | |
print(f"Error saving lead source: {e}") | |
# Update the update_search_term_status function to handle term as string | |
def update_search_term_status(search_term_id, new_status, processed_leads=None): | |
new_status = validate_status(new_status, ["pending", "completed"]) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
if processed_leads is not None: | |
processed_leads = validate_num_results(processed_leads) | |
cursor.execute(""" | |
UPDATE search_terms | |
SET status = ?, processed_leads = ?, last_processed_at = CURRENT_TIMESTAMP | |
WHERE id = ? | |
""", (new_status, processed_leads, search_term_id)) | |
else: | |
cursor.execute(""" | |
UPDATE search_terms | |
SET status = ?, last_processed_at = CURRENT_TIMESTAMP | |
WHERE id = ? | |
""", (new_status, search_term_id)) | |
conn.commit() | |
conn.close() | |
# Update the manual_search_wrapper function | |
def manual_search_wrapper(term, num_results, campaign_id, search_type="All Leads"): | |
results = [] | |
try: | |
print(f"Starting search for term: {term}") | |
search_urls = list(search(term, num=num_results, stop=num_results, pause=2)) | |
print(f"Found {len(search_urls)} URLs") | |
for url in search_urls: | |
try: | |
print(f"Processing URL: {url}") | |
response = session.get(url, timeout=10) | |
response.encoding = 'utf-8' | |
soup = BeautifulSoup(response.text, 'html.parser') | |
title = soup.title.string if soup.title else "" | |
meta_description = soup.find('meta', attrs={'name': 'description'}) | |
meta_description = meta_description['content'] if meta_description else "" | |
emails = find_emails(response.text) | |
phone_numbers = extract_phone_numbers(response.text) | |
meta_tags = [str(tag) for tag in soup.find_all('meta')] | |
content = extract_visible_text(soup) | |
print(f"Found {len(emails)} emails and {len(phone_numbers)} phone numbers on {url}") | |
for email in emails: | |
if is_valid_email(email): | |
tags = [] | |
if is_probable_blog_or_directory(soup, email): | |
tags.append("blog-directory") | |
else: | |
tags.append("company") | |
if search_type == "All Leads" or (search_type == "Exclude Probable Blogs/Directories" and "blog-directory" not in tags): | |
lead_id = save_lead(email, phone_numbers[0] if phone_numbers else None, None, None, None, None) | |
save_lead_source(lead_id, campaign_id, url, title, meta_description, response.status_code, | |
str(response.elapsed), meta_tags, phone_numbers, content, tags) | |
results.append([email, url, title, meta_description, tags]) | |
print(f"Valid email found: {email}") | |
if len(results) >= num_results: | |
break | |
except Exception as e: | |
logging.error(f"Error processing {url}: {e}") | |
print(f"Error processing {url}: {e}") | |
except Exception as e: | |
logging.error(f"Error in manual search: {e}") | |
print(f"Error in manual search: {e}") | |
print(f"Search completed. Found {len(results)} results.") | |
return results[:num_results] | |
# Function to fetch leads | |
def fetch_leads(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
SELECT l.id, l.email, l.phone, l.first_name, l.last_name, l.company, l.job_title, | |
st.term as search_term, ls.url as source_url, ls.page_title, ls.meta_description, | |
ls.phone_numbers, ls.content, ls.tags | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
JOIN search_terms st ON ls.search_term_id = st.id | |
ORDER BY l.id DESC | |
''') | |
rows = cursor.fetchall() | |
conn.close() | |
return pd.DataFrame(rows, columns=["ID", "Email", "Phone", "First Name", "Last Name", "Company", "Job Title", | |
"Search Term", "Source URL", "Page Title", "Meta Description", | |
"Phone Numbers", "Content", "Tags"]) | |
# Add this function to fetch search terms for a specific campaign | |
def fetch_search_terms_for_campaign(campaign_id): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT id, term FROM search_terms WHERE campaign_id = ?', (campaign_id,)) | |
terms = cursor.fetchall() | |
conn.close() | |
return [{"name": f"{term['id']}: {term['term']}", "value": str(term['id'])} for term in terms] | |
# Update the manual_search_multiple function | |
def manual_search_multiple(terms, num_results, campaign_id): | |
all_results = [] | |
for term in terms: | |
if term.strip(): # Only process non-empty terms | |
results = manual_search_wrapper(term.strip(), num_results, campaign_id) | |
all_results.extend(results) | |
return all_results | |
# New function to get least searched terms | |
def get_least_searched_terms(n): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
SELECT term | |
FROM search_terms | |
ORDER BY processed_leads ASC | |
LIMIT ? | |
''', (n,)) | |
terms = [row[0] for row in cursor.fetchall()] | |
conn.close() | |
return terms | |
# Streamlit app | |
def get_knowledge_base(project_id): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT * FROM knowledge_base WHERE project_id = ?", (project_id,)) | |
knowledge = cursor.fetchone() | |
conn.close() | |
return knowledge | |
def update_knowledge_base(project_id, data): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT OR REPLACE INTO knowledge_base ( | |
project_id, name, bio, values, contact_name, contact_role, contact_email, | |
company_description, company_mission, company_target_market, company_other, | |
product_name, product_description, product_target_customer, product_other, | |
other_context, example_email, complete_version, medium_version, small_version, | |
temporal_version, updated_at | |
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP) | |
""", (project_id, *data)) | |
conn.commit() | |
conn.close() | |
def knowledge_base_view(): | |
st.header("Knowledge Base") | |
projects = fetch_projects() | |
selected_project = st.selectbox("Select Project", options=projects) | |
project_id = int(selected_project.split(":")[0]) | |
knowledge = get_knowledge_base(project_id) | |
with st.form("knowledge_base_form"): | |
col1, col2 = st.columns(2) | |
with col1: | |
name = st.text_input("Name", value=knowledge[2] if knowledge else "") | |
bio = st.text_area("Bio", value=knowledge[3] if knowledge else "") | |
values = st.text_area("Values", value=knowledge[4] if knowledge else "") | |
with col2: | |
contact_name = st.text_input("Contact Name", value=knowledge[5] if knowledge else "") | |
contact_role = st.text_input("Contact Role", value=knowledge[6] if knowledge else "") | |
contact_email = st.text_input("Contact Email", value=knowledge[7] if knowledge else "") | |
st.subheader("Company Information") | |
company_description = st.text_area("Company Description", value=knowledge[8] if knowledge else "") | |
company_mission = st.text_area("Company Mission/Vision", value=knowledge[9] if knowledge else "") | |
company_target_market = st.text_area("Company Target Market", value=knowledge[10] if knowledge else "") | |
company_other = st.text_area("Company Other", value=knowledge[11] if knowledge else "") | |
st.subheader("Product Information") | |
product_name = st.text_input("Product Name", value=knowledge[12] if knowledge else "") | |
product_description = st.text_area("Product Description", value=knowledge[13] if knowledge else "") | |
product_target_customer = st.text_area("Product Target Customer", value=knowledge[14] if knowledge else "") | |
product_other = st.text_area("Product Other", value=knowledge[15] if knowledge else "") | |
other_context = st.text_area("Other Context", value=knowledge[16] if knowledge else "") | |
example_email = st.text_area("Example Reference Email", value=knowledge[17] if knowledge else "") | |
submit_button = st.form_submit_button("Save Knowledge Base") | |
if submit_button: | |
data = ( | |
name, bio, values, contact_name, contact_role, contact_email, | |
company_description, company_mission, company_target_market, company_other, | |
product_name, product_description, product_target_customer, product_other, | |
other_context, example_email, "", "", "", "" | |
) | |
update_knowledge_base(project_id, data) | |
st.success("Knowledge base updated successfully!") | |
if knowledge: | |
st.subheader("AI Context Versions") | |
st.text_area("Complete Version", value=knowledge[18], height=200, disabled=True) | |
st.text_area("Medium Version", value=knowledge[19], height=150, disabled=True) | |
st.text_area("Small Version", value=knowledge[20], height=100, disabled=True) | |
st.text_area("Temporal Version", value=knowledge[21], height=100, disabled=True) | |
# Update the search_terms function | |
def search_terms(): | |
st.header("Search Terms") | |
# Add a selector for different views | |
view_option = st.radio("Select View", ["Summary", "Basic List", "Search Term Groups"]) | |
if view_option == "Summary": | |
display_search_term_summary() | |
elif view_option == "Basic List": | |
display_basic_search_terms_list() | |
else: | |
display_search_term_groups() | |
def display_search_term_summary(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM search_terms") | |
total_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_terms WHERE group_id IS NOT NULL") | |
grouped_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_term_groups") | |
total_groups = cursor.fetchone()[0] | |
conn.close() | |
st.subheader("Search Terms Summary") | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Search Terms", total_terms) | |
col2.metric("Grouped Terms", grouped_terms) | |
col3.metric("Total Groups", total_groups) | |
st.subheader("Search Term Groups") | |
groups_data = fetch_search_term_groups_data() | |
sort_option = st.selectbox("Sort groups by", ["Name", "Number of Terms"]) | |
reverse_sort = st.checkbox("Reverse sort order") | |
if sort_option == "Name": | |
groups_data.sort(key=lambda x: x['group_name'], reverse=reverse_sort) | |
else: | |
groups_data.sort(key=lambda x: x['term_count'], reverse=reverse_sort) | |
for group in groups_data: | |
with st.expander(f"{group['group_name']} (Terms: {group['term_count']}, Leads: {group['total_leads']})"): | |
st.write(f"Description: {group['description']}") | |
st.write("Search Terms:") | |
terms_df = pd.DataFrame(group['terms']) | |
st.dataframe(terms_df) | |
st.header("Search Terms") | |
# Add a selector for different views | |
view_option = st.radio("Select View", ["Basic List", "Search Term Groups"]) | |
# Display statistics | |
display_search_term_statistics() | |
if view_option == "Basic List": | |
display_basic_search_terms_list() | |
else: | |
display_search_term_groups() | |
# New function to display search term statistics | |
def display_search_term_statistics(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM search_terms") | |
total_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_terms WHERE group_id IS NOT NULL") | |
grouped_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_term_groups") | |
total_groups = cursor.fetchone()[0] | |
conn.close() | |
st.subheader("Search Terms Summary") | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Search Terms", total_terms) | |
col2.metric("Grouped Terms", grouped_terms) | |
col3.metric("Total Groups", total_groups) | |
# Pruning | |
if st.button("Prune Empty Terms"): | |
pruned_count = prune_empty_search_terms() | |
st.success(f"Pruned {pruned_count} search terms with no associated leads.") | |
# Undo prune | |
if 'last_pruned' in st.session_state and st.session_state.last_pruned > 0: | |
if st.button("Undo Last Prune"): | |
undo_last_prune() | |
st.success(f"Undone the last prune of {st.session_state.last_pruned} search terms.") | |
del st.session_state.last_pruned | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM search_terms") | |
total_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_terms WHERE group_id IS NOT NULL") | |
grouped_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_term_groups") | |
total_groups = cursor.fetchone()[0] | |
conn.close() | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Search Terms", total_terms) | |
col2.metric("Grouped Terms", grouped_terms) | |
col3.metric("Total Groups", total_groups) | |
if st.button("Prune Empty Terms"): | |
pruned_count = prune_empty_search_terms() | |
st.success(f"Pruned {pruned_count} search terms with no associated leads.") | |
# New function to prune empty search terms | |
def prune_empty_search_terms(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
DELETE FROM search_terms | |
WHERE id NOT IN ( | |
SELECT DISTINCT search_term_id | |
FROM lead_sources | |
) | |
''') | |
pruned_count = cursor.rowcount | |
conn.commit() | |
conn.close() | |
return pruned_count | |
def undo_last_prune(): | |
# Implement undo logic for pruned terms | |
pass | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
DELETE FROM search_terms | |
WHERE id NOT IN ( | |
SELECT DISTINCT search_term_id | |
FROM lead_sources | |
) | |
''') | |
pruned_count = cursor.rowcount | |
conn.commit() | |
conn.close() | |
return pruned_count | |
if 'last_pruned' in st.session_state and st.session_state.last_pruned > 0: | |
if st.button("Undo Last Prune"): | |
undo_last_prune() | |
st.success(f"Undone the last prune of {st.session_state.last_pruned} search terms.") | |
del st.session_state.last_pruned | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
DELETE FROM search_terms | |
WHERE id NOT IN ( | |
SELECT DISTINCT search_term_id | |
FROM lead_sources | |
) | |
""") | |
pruned_count = cursor.rowcount | |
conn.commit() | |
conn.close() | |
return pruned_count | |
# Update the display_search_term_groups function | |
def display_search_term_groups(): | |
st.subheader("Search Term Groups") | |
# Fetch search term groups data | |
groups_data = fetch_search_term_groups_data() | |
# Sort options | |
sort_option = st.selectbox("Sort groups by", ["Name", "Number of Terms"]) | |
reverse_sort = st.checkbox("Reverse sort order") | |
if sort_option == "Name": | |
groups_data.sort(key=lambda x: x['group_name'], reverse=reverse_sort) | |
else: | |
groups_data.sort(key=lambda x: x['term_count'], reverse=reverse_sort) | |
# Display groups and their terms | |
for group in groups_data: | |
with st.expander(f"{group['group_name']} (Terms: {group['term_count']}, Leads: {group['total_leads']})"): | |
st.write(f"Description: {group['description']}") | |
st.write("Search Terms:") | |
terms_df = pd.DataFrame(group['terms']) | |
# Allow sorting | |
sort_column = st.selectbox(f"Sort by (Group {group['id']})", terms_df.columns) | |
sort_order = st.radio(f"Order (Group {group['id']})", ["Ascending", "Descending"]) | |
sorted_df = terms_df.sort_values(sort_column, ascending=(sort_order == "Ascending")) | |
st.dataframe(sorted_df) | |
# Delete group button | |
if st.button(f"Delete Group {group['id']}"): | |
delete_search_term_group(group['id']) | |
st.success(f"Group {group['id']} deleted successfully.") | |
st.rerun() | |
# Multi-select and assign to group | |
st.subheader("Assign Terms to Group") | |
all_terms = fetch_all_search_terms() | |
selected_terms = st.multiselect("Select terms to assign", all_terms) | |
new_group_name = st.text_input("New group name (leave empty to use existing)") | |
existing_groups = [g['group_name'] for g in groups_data] | |
target_group = st.selectbox("Select target group", [""] + existing_groups) | |
if st.button("Assign to Group"): | |
if new_group_name: | |
group_id = create_search_term_group(new_group_name) | |
elif target_group: | |
group_id = next(g['id'] for g in groups_data if g['group_name'] == target_group) | |
else: | |
st.error("Please provide a new group name or select an existing group.") | |
return | |
assign_terms_to_group(selected_terms, group_id) | |
st.success(f"Assigned {len(selected_terms)} terms to the group.") | |
st.rerun() | |
# Function to fetch leads | |
def fetch_leads(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
SELECT l.id, l.email, l.phone, l.first_name, l.last_name, l.company, l.job_title, | |
st.term as search_term, ls.url as source_url, ls.page_title, ls.meta_description, | |
ls.phone_numbers, ls.content, ls.tags | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
JOIN search_terms st ON ls.search_term_id = st.id | |
ORDER BY l.id DESC | |
''') | |
rows = cursor.fetchall() | |
conn.close() | |
return pd.DataFrame(rows, columns=["ID", "Email", "Phone", "First Name", "Last Name", "Company", "Job Title", | |
"Search Term", "Source URL", "Page Title", "Meta Description", | |
"Phone Numbers", "Content", "Tags"]) | |
# Add this function to fetch search terms for a specific campaign | |
def fetch_search_terms_for_campaign(campaign_id): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT id, term FROM search_terms WHERE campaign_id = ?', (campaign_id,)) | |
terms = cursor.fetchall() | |
conn.close() | |
return [{"name": f"{term['id']}: {term['term']}", "value": str(term['id'])} for term in terms] | |
# Update the manual_search_multiple function | |
def manual_search_multiple(terms, num_results, campaign_id): | |
all_results = [] | |
for term in terms: | |
if term.strip(): # Only process non-empty terms | |
results = manual_search_wrapper(term.strip(), num_results, campaign_id) | |
all_results.extend(results) | |
return all_results | |
# New function to get least searched terms | |
def get_least_searched_terms(n): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
SELECT term | |
FROM search_terms | |
ORDER BY processed_leads ASC | |
LIMIT ? | |
''', (n,)) | |
terms = [row[0] for row in cursor.fetchall()] | |
conn.close() | |
return terms | |
# Streamlit app | |
def get_knowledge_base(project_id): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT * FROM knowledge_base WHERE project_id = ?", (project_id,)) | |
knowledge = cursor.fetchone() | |
conn.close() | |
return knowledge | |
def update_knowledge_base(project_id, data): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT OR REPLACE INTO knowledge_base ( | |
project_id, name, bio, values, contact_name, contact_role, contact_email, | |
company_description, company_mission, company_target_market, company_other, | |
product_name, product_description, product_target_customer, product_other, | |
other_context, example_email, complete_version, medium_version, small_version, | |
temporal_version, updated_at | |
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP) | |
""", (project_id, *data)) | |
conn.commit() | |
conn.close() | |
def knowledge_base_view(): | |
st.header("Knowledge Base") | |
projects = fetch_projects() | |
selected_project = st.selectbox("Select Project", options=projects) | |
project_id = int(selected_project.split(":")[0]) | |
knowledge = get_knowledge_base(project_id) | |
with st.form("knowledge_base_form"): | |
col1, col2 = st.columns(2) | |
with col1: | |
name = st.text_input("Name", value=knowledge[2] if knowledge else "") | |
bio = st.text_area("Bio", value=knowledge[3] if knowledge else "") | |
values = st.text_area("Values", value=knowledge[4] if knowledge else "") | |
with col2: | |
contact_name = st.text_input("Contact Name", value=knowledge[5] if knowledge else "") | |
contact_role = st.text_input("Contact Role", value=knowledge[6] if knowledge else "") | |
contact_email = st.text_input("Contact Email", value=knowledge[7] if knowledge else "") | |
st.subheader("Company Information") | |
company_description = st.text_area("Company Description", value=knowledge[8] if knowledge else "") | |
company_mission = st.text_area("Company Mission/Vision", value=knowledge[9] if knowledge else "") | |
company_target_market = st.text_area("Company Target Market", value=knowledge[10] if knowledge else "") | |
company_other = st.text_area("Company Other", value=knowledge[11] if knowledge else "") | |
st.subheader("Product Information") | |
product_name = st.text_input("Product Name", value=knowledge[12] if knowledge else "") | |
product_description = st.text_area("Product Description", value=knowledge[13] if knowledge else "") | |
product_target_customer = st.text_area("Product Target Customer", value=knowledge[14] if knowledge else "") | |
product_other = st.text_area("Product Other", value=knowledge[15] if knowledge else "") | |
other_context = st.text_area("Other Context", value=knowledge[16] if knowledge else "") | |
example_email = st.text_area("Example Reference Email", value=knowledge[17] if knowledge else "") | |
submit_button = st.form_submit_button("Save Knowledge Base") | |
if submit_button: | |
data = ( | |
name, bio, values, contact_name, contact_role, contact_email, | |
company_description, company_mission, company_target_market, company_other, | |
product_name, product_description, product_target_customer, product_other, | |
other_context, example_email, "", "", "", "" | |
) | |
update_knowledge_base(project_id, data) | |
st.success("Knowledge base updated successfully!") | |
if knowledge: | |
st.subheader("AI Context Versions") | |
st.text_area("Complete Version", value=knowledge[18], height=200, disabled=True) | |
st.text_area("Medium Version", value=knowledge[19], height=150, disabled=True) | |
st.text_area("Small Version", value=knowledge[20], height=100, disabled=True) | |
st.text_area("Temporal Version", value=knowledge[21], height=100, disabled=True) | |
# Update the main function to include the new knowledge_base_view | |
from openai import OpenAI | |
# Initialize the OpenAI client | |
client = OpenAI(api_key="sk-1234") | |
client.base_url = "https://openai-proxy-kl3l.onrender.com" | |
import os | |
from openai import OpenAI | |
# Initialize the OpenAI client | |
client = OpenAI( | |
api_key="sk-1234", | |
base_url="https://openai-proxy-kl3l.onrender.com" | |
) | |
# The model to use | |
openai_model = "gpt-3.5-turbo" | |
from openai import OpenAI | |
import numpy as np | |
from sklearn.cluster import KMeans | |
from sklearn.feature_extraction.text import TfidfVectorizer | |
# Initialize the OpenAI client | |
client = OpenAI( | |
api_key="sk-1234", | |
base_url="https://openai-proxy-kl3l.onrender.com" | |
) | |
# At the beginning of your script, add: | |
ai_request_logs = [] | |
def autoclient_ai_view(): | |
st.header("AutoclientAI") | |
# Display condensed knowledge base | |
kb_info = get_knowledge_base_info() | |
st.subheader("Knowledge Base Summary") | |
st.json(kb_info) | |
# Create tabs | |
tab1, tab2, tab3, tab4 = st.tabs(["Optimize Existing Groups", "Create New Groups", "Adjust Email Templates", "Optimize Search Terms"]) | |
with tab1: | |
optimize_existing_groups() | |
with tab2: | |
create_new_groups() | |
with tab3: | |
adjust_email_template() | |
with tab4: | |
optimize_search_terms() | |
# Display AI request logs | |
if ai_request_logs: | |
st.subheader("AI Request Logs") | |
for log in ai_request_logs: | |
st.text(log) | |
else: | |
st.info("No AI request logs available.") | |
def optimize_existing_groups(): | |
groups = fetch_search_term_groups() | |
if not groups: | |
st.warning("No search term groups found.") | |
return | |
selected_group = st.selectbox("Select a group to optimize", options=groups) | |
if st.button("Optimize Selected Group"): | |
with st.spinner("Optimizing group..."): | |
try: | |
group_data = get_group_data(selected_group) | |
kb_info = get_knowledge_base_info() | |
optimized_data = classify_search_terms(group_data['search_terms'], kb_info) | |
st.subheader("Optimized Search Terms") | |
for category, terms in optimized_data.items(): | |
st.write(f"**{category}:**") | |
new_terms = st.text_area(f"Edit terms for {category}:", value="\n".join(terms)) | |
optimized_data[category] = new_terms.split("\n") | |
if st.button("Save Optimized Group"): | |
save_optimized_group(selected_group, optimized_data) | |
st.success("Group optimized and saved successfully!") | |
except Exception as e: | |
st.error(f"An error occurred during optimization: {str(e)}") | |
logging.error(f"Error in optimize_existing_groups: {str(e)}") | |
def create_new_groups(): | |
st.subheader("Create New Search Term Group") | |
group_name = st.text_input("Group Name") | |
group_description = st.text_area("Group Description") | |
search_terms = st.text_area("Enter search terms (one per line)") | |
if st.button("Create Group"): | |
if not group_name: | |
st.error("Please provide a group name.") | |
return | |
terms_list = [term.strip() for term in search_terms.split('\n') if term.strip()] | |
try: | |
group_id = create_search_term_group(group_name, group_description) | |
assign_terms_to_group(terms_list, group_id) | |
st.success(f"Group '{group_name}' created successfully with {len(terms_list)} terms.") | |
except Exception as e: | |
st.error(f"An error occurred while creating the group: {str(e)}") | |
logging.error(f"Error in create_new_groups: {str(e)}") | |
def adjust_email_template(): | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found.") | |
return | |
selected_template = st.selectbox("Select a template to adjust", options=templates) | |
template_data = get_template_data(selected_template.split(':')[0]) | |
if template_data: | |
st.subheader("Current Template") | |
st.text_input("Subject", value=template_data['subject']) | |
st.text_area("Content", value=template_data['body_content'], height=300) | |
st.subheader("Adjust Template") | |
new_subject = st.text_input("New Subject", value=template_data['subject']) | |
new_content = st.text_area("New Content", value=template_data['body_content'], height=300) | |
if st.button("Save Adjusted Template"): | |
try: | |
save_adjusted_template(selected_template.split(':')[0], new_subject, new_content) | |
st.success("Template adjusted and saved successfully!") | |
except Exception as e: | |
st.error(f"An error occurred while saving the template: {str(e)}") | |
logging.error(f"Error in adjust_email_template: {str(e)}") | |
def optimize_search_terms(): | |
st.subheader("Optimize Search Terms") | |
current_terms = st.text_area("Enter current search terms (one per line):") | |
if st.button("Optimize Search Terms"): | |
if not current_terms.strip(): | |
st.error("Please enter some search terms to optimize.") | |
return | |
with st.spinner("Optimizing search terms..."): | |
try: | |
kb_info = get_knowledge_base_info() | |
optimized_terms = generate_optimized_search_terms(current_terms.split('\n'), kb_info) | |
st.subheader("Optimized Search Terms") | |
st.write("\n".join(optimized_terms)) | |
if st.button("Save Optimized Terms"): | |
save_optimized_search_terms(optimized_terms) | |
st.success("Optimized search terms saved successfully!") | |
except Exception as e: | |
st.error(f"An error occurred during optimization: {str(e)}") | |
logging.error(f"Error in optimize_search_terms: {str(e)}") | |
def classify_search_terms(search_terms, kb_info): | |
prompt = f""" | |
As an expert in lead generation and email marketing, classify the following search terms into strategic groups: | |
Search Terms: {', '.join(search_terms)} | |
Knowledge Base Info: | |
{kb_info} | |
Create groups that allow for tailored, personalized email content. Consider the product/service features, target audience, and potential customer pain points. Groups should be specific enough for customization but broad enough to be efficient. Always include a 'low_quality_search_terms' category for irrelevant or overly broad terms. | |
Respond with category names as keys and lists of search terms as values. | |
""" | |
response = client.chat.completions.create( | |
model="gpt-4", | |
messages=[ | |
{"role": "system", "content": "You are an AI assistant specializing in strategic search term classification for targeted email marketing campaigns."}, | |
{"role": "user", "content": prompt} | |
] | |
) | |
log_ai_request("classify_search_terms", prompt, response.choices[0].message.content) | |
return eval(response.choices[0].message.content) | |
def generate_email_template(terms, kb_info): | |
prompt = f""" | |
Create an email template for the following search terms: | |
Search Terms: {', '.join(terms)} | |
Knowledge Base Info: | |
{kb_info} | |
Guidelines: | |
1. Focus on benefits to the reader | |
2. Address potential customer doubts and fears | |
3. Include clear CTAs at the beginning and end | |
4. Use a natural, conversational tone | |
5. Be concise but impactful | |
6. Use minimal formatting - remember this is an email, not a landing page | |
Provide only the email body content in HTML format, excluding <body> tags. Use <p>, <strong>, <em>, and <a> tags as needed. | |
""" | |
response = client.chat.completions.create( | |
model="gpt-4", | |
messages=[ | |
{"role": "system", "content": "You are an AI assistant specializing in creating high-converting email templates for targeted marketing campaigns."}, | |
{"role": "user", "content": prompt} | |
] | |
) | |
log_ai_request("generate_email_template", prompt, response.choices[0].message.content) | |
return response.choices[0].message.content | |
def adjust_email_template_api(current_template, adjustment_prompt, kb_info): | |
prompt = f""" | |
Adjust the following email template based on the given instructions: | |
Current Template: | |
{current_template} | |
Adjustment Instructions: | |
{adjustment_prompt} | |
Knowledge Base Info: | |
{kb_info} | |
Guidelines: | |
1. Maintain focus on conversion and avoiding spam filters | |
2. Preserve the natural, conversational tone | |
3. Ensure benefits to the reader remain highlighted | |
4. Continue addressing potential customer doubts and fears | |
5. Keep clear CTAs at the beginning and end | |
6. Remain concise and impactful | |
7. Maintain minimal formatting suitable for an email | |
Provide only the adjusted email body content in HTML format, excluding <body> tags. | |
""" | |
response = client.chat.completions.create( | |
model="gpt-4", | |
messages=[ | |
{"role": "system", "content": "You are an AI assistant specializing in refining high-converting email templates for targeted marketing campaigns."}, | |
{"role": "user", "content": prompt} | |
] | |
) | |
log_ai_request("adjust_email_template_api", prompt, response.choices[0].message.content) | |
return response.choices[0].message.content | |
def generate_optimized_search_terms(current_terms, kb_info): | |
prompt = f""" | |
Optimize the following search terms for targeted email campaigns: | |
Current Terms: {', '.join(current_terms)} | |
Knowledge Base Info: | |
{kb_info} | |
Guidelines: | |
1. Focus on terms likely to attract high-quality leads | |
2. Consider product/service features, target audience, and customer pain points | |
3. Optimize for specificity and relevance | |
4. Think about how each term could lead to a compelling email strategy | |
5. Remove or improve low-quality or overly broad terms | |
6. Add new, highly relevant terms based on the knowledge base information | |
Provide a list of optimized search terms, aiming for quality over quantity. | |
""" | |
response = client.chat.completions.create( | |
model="gpt-4", | |
messages=[ | |
{"role": "system", "content": "You are an AI assistant specializing in optimizing search terms for targeted email marketing campaigns."}, | |
{"role": "user", "content": prompt} | |
] | |
) | |
log_ai_request("generate_optimized_search_terms", prompt, response.choices[0].message.content) | |
return response.choices[0].message.content.split('\n') | |
def fetch_search_term_groups(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT id, name FROM search_term_groups") | |
groups = [f"{row['id']}:{row['name']}" for row in cursor.fetchall()] | |
conn.close() | |
return groups | |
def get_group_data(group_id): | |
if not group_id: | |
return None | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
group_id = group_id.split(':')[0] | |
cursor.execute("SELECT name, description, email_template FROM search_term_groups WHERE id = ?", (group_id,)) | |
group_data = dict(cursor.fetchone()) | |
cursor.execute("SELECT term FROM search_terms WHERE group_id = ?", (group_id,)) | |
group_data['search_terms'] = [row['term'] for row in cursor.fetchall()] | |
conn.close() | |
return group_data | |
def save_optimized_group(group_id, optimized_data): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
group_id = group_id.split(':')[0] | |
# Delete existing terms for this group | |
cursor.execute("DELETE FROM search_terms WHERE group_id = ?", (group_id,)) | |
# Insert new terms | |
for category, terms in optimized_data.items(): | |
for term in terms: | |
cursor.execute("INSERT INTO search_terms (term, group_id, category) VALUES (?, ?, ?)", | |
(term, group_id, category)) | |
conn.commit() | |
conn.close() | |
def fetch_all_search_terms(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT term FROM search_terms") | |
terms = [row['term'] for row in cursor.fetchall()] | |
conn.close() | |
return terms | |
def save_new_group(category, terms, email_template): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("INSERT INTO search_term_groups (name, email_template) VALUES (?, ?)", (category, email_template)) | |
group_id = cursor.lastrowid | |
for term in terms: | |
cursor.execute("INSERT INTO search_terms (term, group_id, category) VALUES (?, ?, ?)", | |
(term, group_id, category)) | |
conn.commit() | |
conn.close() | |
def save_adjusted_template(group_id, adjusted_template): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
group_id = group_id.split(':')[0] | |
cursor.execute("UPDATE search_term_groups SET email_template = ? WHERE id = ?", | |
(adjusted_template, group_id)) | |
conn.commit() | |
conn.close() | |
def get_knowledge_base_info(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
SELECT kb_name, kb_bio, kb_values, contact_name, contact_role, contact_email, | |
company_description, company_mission, company_target_market, company_other, | |
product_name, product_description, product_target_customer, product_other, | |
other_context, example_email | |
FROM knowledge_base | |
ORDER BY id DESC LIMIT 1 | |
""") | |
result = cursor.fetchone() | |
conn.close() | |
if result: | |
return { | |
"kb_name": result[0], | |
"kb_bio": result[1], | |
"kb_values": result[2], | |
"contact_name": result[3], | |
"contact_role": result[4], | |
"contact_email": result[5], | |
"company_description": result[6], | |
"company_mission": result[7], | |
"company_target_market": result[8], | |
"company_other": result[9], | |
"product_name": result[10], | |
"product_description": result[11], | |
"product_target_customer": result[12], | |
"product_other": result[13], | |
"other_context": result[14], | |
"example_email": result[15] | |
} | |
else: | |
return {} | |
def save_optimized_search_terms(optimized_terms): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
for term in optimized_terms: | |
cursor.execute("INSERT INTO search_terms (term) VALUES (?)", (term,)) | |
conn.commit() | |
conn.close() | |
def get_last_n_search_terms(n): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(f"SELECT term FROM search_terms ORDER BY id DESC LIMIT {n}") | |
terms = [row[0] for row in cursor.fetchall()] | |
conn.close() | |
return terms | |
def get_random_leads(n, from_last): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(f""" | |
SELECT l.email, st.term | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
JOIN search_terms st ON ls.search_term_id = st.id | |
ORDER BY l.id DESC LIMIT {from_last} | |
""") | |
leads = cursor.fetchall() | |
conn.close() | |
return random.sample(leads, min(n, len(leads))) | |
def format_leads_for_prompt(leads): | |
return "\n".join([f"{lead[0]} - {lead[1]}" for lead in leads]) | |
def save_new_search_terms(terms): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
for term in terms: | |
cursor.execute("INSERT INTO search_terms (term) VALUES (?)", (term,)) | |
conn.commit() | |
conn.close() | |
def save_new_email_template(template): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
INSERT INTO message_templates (template_name, subject, body_content) | |
VALUES (?, ?, ?) | |
""", ("AI Generated Template", "AI Subject", template)) | |
conn.commit() | |
conn.close() | |
def main(): | |
st.title("AUTOCLIENT") | |
st.sidebar.title("Navigation") | |
pages = [ | |
"Manual Search", | |
"Bulk Search", | |
"Bulk Send", | |
"View Leads", | |
"Search Terms", | |
"Message Templates", | |
"View Sent Messages", | |
"Projects & Campaigns", | |
"Knowledge Base", | |
"AutoclientAI", | |
"Automation Control" | |
] | |
# Control page selection | |
if 'current_page' not in st.session_state: | |
st.session_state.current_page = "Manual Search" | |
st.session_state.current_page = st.sidebar.radio("Go to", pages, index=pages.index(st.session_state.current_page)) | |
# Call appropriate function based on the selected page | |
if st.session_state.current_page == "Manual Search": | |
manual_search_page() | |
elif st.session_state.current_page == "Bulk Search": | |
bulk_search_page() | |
elif st.session_state.current_page == "Bulk Send": | |
bulk_send_page() | |
elif st.session_state.current_page == "View Leads": | |
view_leads() | |
elif st.session_state.current_page == "Search Terms": | |
search_terms() | |
elif st.session_state.current_page == "Message Templates": | |
message_templates() | |
elif st.session_state.current_page == "View Sent Messages": | |
view_sent_messages() | |
elif st.session_state.current_page == "Projects & Campaigns": | |
projects_and_campaigns() | |
elif st.session_state.current_page == "Knowledge Base": | |
knowledge_base_view() | |
elif st.session_state.current_page == "AutoclientAI": | |
autoclient_ai_view() | |
elif st.session_state.current_page == "Automation Control": | |
automation_view() | |
def display_result_card(result, index): | |
with st.container(): | |
st.markdown(f""" | |
<div style="border:1px solid #e0e0e0; border-radius:10px; padding:15px; margin-bottom:10px;"> | |
<h3 style="color:#1E90FF;">Result {index + 1}</h3> | |
<p><strong>Email:</strong> {result[0]}</p> | |
<p><strong>Source:</strong> <a href="{result[1]}" target="_blank">{result[1][:50]}...</a></p> | |
<p><strong>Tags:</strong> {', '.join(result[4])}</p> | |
</div> | |
""", unsafe_allow_html=True) | |
def manual_search_page(): | |
st.header("Manual Search") | |
# Fetch the last 5 search terms to use as a reference or for input suggestions. | |
last_5_terms = get_last_5_search_terms() | |
# Separate UI for single and multiple term searches using tabs | |
tab1, tab2 = st.tabs(["Single Term Search", "Multiple Terms Search"]) | |
# ---- Single Term Search Tab ---- | |
with tab1: | |
with st.form(key="single_search_form"): | |
search_term = st.text_input("Search Term") | |
num_results = st.slider("Number of Results", min_value=10, max_value=200, value=30, step=10) | |
campaign_id = st.selectbox("Select Campaign", options=fetch_campaigns()) | |
search_type = st.selectbox("Search Type", options=["All Leads", "Exclude Probable Blogs/Directories"]) | |
submit_button = st.form_submit_button(label="Search") | |
if submit_button: | |
st.session_state.single_search_started = True | |
st.session_state.single_search_results = [] | |
st.session_state.single_search_logs = [] | |
st.session_state.single_search_progress = 0 | |
if 'single_search_started' in st.session_state and st.session_state.single_search_started: | |
# Containers to display search progress, logs, and results. | |
results_container = st.empty() | |
progress_bar = st.progress(st.session_state.single_search_progress) | |
status_text = st.empty() | |
log_container = st.empty() | |
if len(st.session_state.single_search_results) < num_results: | |
with st.spinner("Searching..."): | |
new_results = manual_search_wrapper(search_term, num_results - len(st.session_state.single_search_results), campaign_id.split(":")[0], search_type) | |
st.session_state.single_search_results.extend(new_results) | |
# Log and display progress of each found result. | |
for result in new_results: | |
log = f"Found result: {result[0]} from {result[1]}" | |
st.session_state.single_search_logs.append(log) | |
log_container.text_area("Search Logs", "\n".join(st.session_state.single_search_logs), height=200) | |
st.session_state.single_search_progress = len(st.session_state.single_search_results) / num_results | |
progress_bar.progress(st.session_state.single_search_progress) | |
status_text.text(f"Found {len(st.session_state.single_search_results)} results...") | |
# Display the results dynamically. | |
with results_container.container(): | |
for j, res in enumerate(st.session_state.single_search_results): | |
display_result_card(res, j) | |
time.sleep(0.1) # Small delay for better animation effect. | |
# Search completion status | |
if len(st.session_state.single_search_results) >= num_results: | |
st.success(f"Search completed! Found {len(st.session_state.single_search_results)} results.") | |
st.session_state.single_search_started = False | |
# Display session-specific statistics | |
st.subheader("Search Statistics") | |
st.metric("Total Results Found", len(st.session_state.single_search_results)) | |
st.metric("Unique Domains", len(set(result[0].split('@')[1] for result in st.session_state.single_search_results))) | |
# ---- Multiple Terms Search Tab ---- | |
with tab2: | |
st.subheader("Enter Search Terms") | |
# Allow user to enter multiple search terms (one per line). | |
search_terms_text = st.text_area("Enter one search term per line", height=150, value="\n".join(last_5_terms)) | |
load_button = st.button("Load Terms from Text Area") | |
if load_button: | |
terms_list = [term.strip() for term in search_terms_text.split('\n') if term.strip()] | |
st.session_state.loaded_terms = terms_list | |
st.rerun() # Refresh with loaded terms | |
if 'loaded_terms' not in st.session_state: | |
st.session_state.loaded_terms = [""] * 4 # Default to 4 empty terms | |
num_terms = len(st.session_state.loaded_terms) | |
# Form for multiple search terms | |
with st.form(key="multi_search_form"): | |
search_terms = [st.text_input(f"Search Term {i+1}", value=term, key=f"term_{i}") | |
for i, term in enumerate(st.session_state.loaded_terms)] | |
num_results_multiple = st.slider("Number of Results per Term", min_value=10, max_value=200, value=30, step=10) | |
campaign_id_multiple = st.selectbox("Select Campaign for Multiple Search", options=fetch_campaigns(), key="multi_campaign") | |
search_type = st.selectbox("Search Type", options=["All Leads", "Exclude Probable Blogs/Directories"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
submit_button = st.form_submit_button(label="Search All Terms") | |
with col2: | |
fill_button = st.form_submit_button(label="Fill with Least Searched Terms") | |
if submit_button: | |
st.session_state.multi_search_started = True | |
st.session_state.multi_search_results = [] | |
st.session_state.multi_search_logs = [] | |
st.session_state.multi_search_progress = 0 | |
st.session_state.multi_search_terms = [term for term in search_terms if term.strip()] | |
if 'multi_search_started' in st.session_state and st.session_state.multi_search_started: | |
# Containers to display multi-search progress, logs, and results. | |
results_container = st.empty() | |
progress_bar = st.progress(st.session_state.multi_search_progress) | |
status_text = st.empty() | |
log_container = st.empty() | |
with st.spinner("Searching..."): | |
all_results = [] | |
logs = [] | |
total_terms = len(st.session_state.multi_search_terms) | |
# Iterate through multiple search terms and gather results | |
for term_index, term in enumerate(st.session_state.multi_search_terms): | |
status_text.text(f"Searching term {term_index + 1} of {total_terms}: {term}") | |
term_results = manual_search_wrapper(term, num_results_multiple, campaign_id_multiple.split(":")[0], search_type) | |
for i, result in enumerate(term_results): | |
all_results.append(result) | |
progress = (term_index * num_results_multiple + i + 1) / (total_terms * num_results_multiple) | |
progress_bar.progress(progress) | |
log = f"Term {term_index + 1}: Found result {i + 1}: {result[0]} from {result[1]}" | |
logs.append(log) | |
log_container.text_area("Search Logs", "\n".join(logs), height=200) | |
# Display each found result dynamically | |
with results_container.container(): | |
for j, res in enumerate(all_results): | |
display_result_card(res, j) | |
time.sleep(0.1) # Small delay for animation effect | |
# Update progress bar after search completion | |
st.session_state.multi_search_progress = 1.0 | |
progress_bar.progress(st.session_state.multi_search_progress) | |
st.success(f"Found {len(all_results)} results across all terms!") | |
# Display statistics specific to this session | |
st.subheader("Search Statistics") | |
st.metric("Total Results Found", len(all_results)) | |
st.metric("Unique Domains", len(set(result[0].split('@')[1] for result in all_results))) | |
st.metric("Search Terms Processed", len(st.session_state.multi_search_terms)) | |
if fill_button: | |
least_searched = get_least_searched_terms(num_terms) | |
st.session_state.loaded_terms = least_searched | |
st.rerun() | |
def bulk_search_page(): | |
st.header("Bulk Search") | |
with st.form(key="bulk_search_form"): | |
num_results = st.slider("Results per term", min_value=10, max_value=200, value=30, step=10) | |
submit_button = st.form_submit_button(label="Start Bulk Search") | |
if submit_button: | |
st.session_state.bulk_search_started = True | |
st.session_state.bulk_search_results = [] | |
st.session_state.bulk_search_logs = [] | |
st.session_state.bulk_search_progress = 0 | |
st.session_state.bulk_search_terms = fetch_search_terms() | |
if 'bulk_search_started' in st.session_state and st.session_state.bulk_search_started: | |
progress_bar = st.progress(st.session_state.bulk_search_progress) | |
status_text = st.empty() | |
log_container = st.empty() | |
results_container = st.empty() | |
with st.spinner("Performing bulk search..."): | |
total_terms = len(st.session_state.bulk_search_terms) | |
for term_index, term_row in enumerate(st.session_state.bulk_search_terms.iterrows()): | |
if term_index < len(st.session_state.bulk_search_results) // num_results: | |
continue | |
term = term_row.Term | |
status_text.text(f"Searching term {term_index + 1} of {total_terms}: {term}") | |
term_results = manual_search_wrapper(term, num_results, term_row.ID, "All Leads") | |
st.session_state.bulk_search_results.extend(term_results) | |
st.session_state.bulk_search_logs.extend([f"Term {term_index + 1}: Found result {i + 1}: {result[0]} from {result[1]}" for i, result in enumerate(term_results)]) | |
st.session_state.bulk_search_progress = (term_index + 1) / total_terms | |
progress_bar.progress(st.session_state.bulk_search_progress) | |
log_container.text_area("Search Logs", "\n".join(st.session_state.bulk_search_logs), height=200) | |
with results_container.container(): | |
for j, res in enumerate(st.session_state.bulk_search_results): | |
display_result_card(res, j) | |
if st.session_state.bulk_search_progress >= 1: | |
st.success(f"Bulk search completed! Found {len(st.session_state.bulk_search_results)} results.") | |
st.session_state.bulk_search_started = False | |
# Display statistics | |
st.subheader("Bulk Search Statistics") | |
st.metric("Total Results Found", len(st.session_state.bulk_search_results)) | |
st.metric("Unique Domains", len(set(result[0].split('@')[1] for result in st.session_state.bulk_search_results))) | |
st.metric("Search Terms Processed", total_terms) | |
def bulk_send_page(): | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found. Please create a template first.") | |
return | |
with st.form(key="bulk_send_form"): | |
template_id = st.selectbox("Select Message Template", options=templates) | |
from_email = st.text_input("From Email", value="Sami Halawa <[email protected]>") | |
reply_to = st.text_input("Reply To", value="[email protected]") | |
send_option = st.radio("Send to:", | |
["All Leads", | |
"All Not Contacted with this Template", | |
"All Not Contacted with Templates from this Campaign", | |
"Selected Search Term Groups"]) | |
if send_option == "Selected Search Term Groups": | |
groups = fetch_search_term_groups() | |
selected_groups = st.multiselect("Select Search Term Groups", options=groups) | |
group_leads = get_leads_count_for_groups(selected_groups) | |
st.write("Leads in selected groups:") | |
for group, count in group_leads.items(): | |
st.write(f"{group}: {count} leads") | |
filter_option = st.radio("Filter:", | |
["Not Filter Out Leads", | |
"Filter Out blog-directory"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
preview_button = st.form_submit_button(label="Preview Email") | |
with col2: | |
send_button = st.form_submit_button(label="Start Bulk Send") | |
if preview_button: | |
preview = get_email_preview(template_id.split(":")[0], from_email, reply_to) | |
st.components.v1.html(preview, height=600, scrolling=True) | |
if send_button: | |
if send_option == "Selected Search Term Groups" and not selected_groups: | |
st.error("Please select at least one search term group.") | |
return | |
st.session_state.bulk_send_started = True | |
st.session_state.bulk_send_logs = [] | |
st.session_state.bulk_send_progress = 0 | |
leads_to_send = fetch_leads_for_bulk_send(template_id.split(":")[0], send_option, filter_option, selected_groups if send_option == "Selected Search Term Groups" else None) | |
st.write(f"Preparing to send emails to {len(leads_to_send)} leads") | |
bulk_send_coroutine = bulk_send(template_id.split(":")[0], from_email, reply_to, leads_to_send) | |
logs = asyncio.run(bulk_send_coroutine) | |
for log in logs: | |
st.write(log) | |
st.success(f"Bulk send completed. Sent {len(leads_to_send)} emails.") | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found. Please create a template first.") | |
return | |
with st.form(key="bulk_send_form"): | |
template_id = st.selectbox("Select Message Template", options=templates) | |
from_email = st.text_input("From Email", value="Sami Halawa <[email protected]>") | |
reply_to = st.text_input("Reply To", value="[email protected]") | |
send_option = st.radio("Send to:", | |
["All Leads", | |
"All Not Contacted with this Template", | |
"All Not Contacted with Templates from this Campaign", | |
"Selected Search Term Groups"]) | |
if send_option == "Selected Search Term Groups": | |
groups = fetch_search_term_groups() | |
selected_groups = st.multiselect("Select Search Term Groups", options=groups) | |
group_leads = get_leads_count_for_groups(selected_groups) | |
st.write("Leads in selected groups:") | |
for group, count in group_leads.items(): | |
st.write(f"{group}: {count} leads") | |
filter_option = st.radio("Filter:", | |
["Not Filter Out Leads", | |
"Filter Out blog-directory"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
preview_button = st.form_submit_button(label="Preview Email") | |
with col2: | |
send_button = st.form_submit_button(label="Start Bulk Send") | |
if preview_button: | |
preview = get_email_preview(template_id.split(":")[0], from_email, reply_to) | |
st.components.v1.html(preview, height=600, scrolling=True) | |
if send_button: | |
if send_option == "Selected Search Term Groups" and not selected_groups: | |
st.error("Please select at least one search term group.") | |
return | |
st.session_state.bulk_send_started = True | |
st.session_state.bulk_send_logs = [] | |
st.session_state.bulk_send_progress = 0 | |
leads_to_send = fetch_leads_for_bulk_send(template_id.split(":")[0], send_option, filter_option, selected_groups if send_option == "Selected Search Term Groups" else None) | |
st.write(f"Preparing to send emails to {len(leads_to_send)} leads") | |
bulk_send_coroutine = bulk_send(template_id.split(":")[0], from_email, reply_to, leads_to_send) | |
logs = asyncio.run(bulk_send_coroutine) | |
for log in logs: | |
st.write(log) | |
st.success(f"Bulk send completed. Sent {len(leads_to_send)} emails.") | |
st.header("Bulk Send") | |
templates = fetch_message_templates() | |
if not templates: | |
st.warning("No message templates found. Please create a template first.") | |
return | |
with st.form(key="bulk_send_form"): | |
template_id = st.selectbox("Select Message Template", options=templates) | |
from_email = st.text_input("From Email", value="Sami Halawa <[email protected]>") | |
reply_to = st.text_input("Reply To", value="[email protected]") | |
send_option = st.radio("Send to:", | |
["All Leads", | |
"All Not Contacted with this Template", | |
"All Not Contacted with Templates from this Campaign", | |
"Selected Search Term Groups"]) | |
if send_option == "Selected Search Term Groups": | |
groups = fetch_search_term_groups() | |
selected_groups = st.multiselect("Select Search Term Groups", options=groups) | |
group_leads = get_leads_count_for_groups(selected_groups) | |
st.write("Leads in selected groups:") | |
for group, count in group_leads.items(): | |
st.write(f"{group}: {count} leads") | |
filter_option = st.radio("Filter:", | |
["Not Filter Out Leads", | |
"Filter Out blog-directory"]) | |
col1, col2 = st.columns(2) | |
with col1: | |
preview_button = st.form_submit_button(label="Preview Email") | |
with col2: | |
send_button = st.form_submit_button(label="Start Bulk Send") | |
if preview_button: | |
preview = get_email_preview(template_id.split(":")[0], from_email, reply_to) | |
st.components.v1.html(preview, height=600, scrolling=True) | |
if send_button: | |
if send_option == "Selected Search Term Groups" and not selected_groups: | |
st.error("Please select at least one search term group.") | |
return | |
st.session_state.bulk_send_started = True | |
st.session_state.bulk_send_logs = [] | |
st.session_state.bulk_send_progress = 0 | |
# Fetch leads based on send_option and filter_option | |
leads_to_send = fetch_leads_for_bulk_send(template_id.split(":")[0], send_option, filter_option, selected_groups if send_option == "Selected Search Term Groups" else None) | |
st.write(f"Preparing to send emails to {len(leads_to_send)} leads") | |
# Perform bulk send | |
bulk_send_coroutine = bulk_send(template_id.split(":")[0], from_email, reply_to, leads_to_send) | |
logs = asyncio.run(bulk_send_coroutine) | |
# Display logs and statistics | |
for log in logs: | |
st.write(log) | |
st.success(f"Bulk send completed. Sent {len(leads_to_send)} emails.") | |
def fetch_leads_for_bulk_send(template_id, send_option, filter_option, selected_groups=None): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
query = """ | |
SELECT DISTINCT l.id, l.email | |
FROM leads l | |
JOIN lead_sources ls ON l.id = ls.lead_id | |
""" | |
if send_option == "All Not Contacted with this Template": | |
query += f""" | |
LEFT JOIN messages m ON l.id = m.lead_id AND m.template_id = {template_id} | |
WHERE m.id IS NULL | |
""" | |
elif send_option == "All Not Contacted with Templates from this Campaign": | |
query += f""" | |
LEFT JOIN messages m ON l.id = m.lead_id | |
LEFT JOIN message_templates mt ON m.template_id = mt.id | |
WHERE m.id IS NULL OR mt.campaign_id != (SELECT campaign_id FROM message_templates WHERE id = {template_id}) | |
""" | |
elif send_option == "Selected Search Term Groups": | |
group_ids = [group.split(":")[0] for group in selected_groups] | |
query += f""" | |
JOIN search_terms st ON ls.search_term_id = st.id | |
WHERE st.group_id IN ({','.join(group_ids)}) | |
""" | |
if filter_option == "Filter Out blog-directory": | |
query += " AND NOT ls.tags LIKE '%blog-directory%'" | |
cursor.execute(query) | |
leads = cursor.fetchall() | |
conn.close() | |
return leads | |
async def bulk_send(template_id, from_email, reply_to, leads): | |
template_id = validate_id(template_id, "template") | |
from_email = validate_email(from_email) | |
reply_to = validate_email(reply_to) | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute('SELECT name, subject, body_content FROM message_templates WHERE id = ?', (template_id,)) | |
template = cursor.fetchone() | |
if not template: | |
return "Template not found" | |
template_name, subject, body_content = template | |
total_leads = len(leads) | |
logs = [ | |
f"Preparing to send emails to {total_leads} leads", | |
f"Template Name: {template_name}", | |
f"Subject: {subject}", | |
f"From Email: {from_email}", | |
f"Reply To: {reply_to}" | |
] | |
for i, (lead_id, email) in enumerate(leads): | |
try: | |
customized_content = customize_email_content(body_content, lead_id) | |
message_id = send_email(email, subject, customized_content, from_email, reply_to) | |
save_message(lead_id, template_id, 'sent', datetime.now(), subject, message_id, customized_content) | |
logs.append(f"Sent email to {email} (Lead ID: {lead_id})") | |
except Exception as e: | |
save_message(lead_id, template_id, 'failed', datetime.now(), subject, None, str(e)) | |
logs.append(f"Failed to send email to {email} (Lead ID: {lead_id}): {e}") | |
progress = (i + 1) / total_leads | |
st.session_state.bulk_send_progress = progress | |
st.session_state.bulk_send_logs = logs | |
time.sleep(0.1) # Add a small delay for UI updates | |
conn.close() | |
return logs | |
def view_leads(): | |
st.header("View Leads") | |
if st.button("Refresh Leads"): | |
st.session_state.leads = fetch_leads() | |
if 'leads' not in st.session_state: | |
st.session_state.leads = fetch_leads() | |
for _, lead in st.session_state.leads.iterrows(): | |
with st.expander(f"Lead: {lead['Email']}"): | |
col1, col2 = st.columns(2) | |
with col1: | |
st.write(f"**ID:** {lead['ID']}") | |
st.write(f"**Email:** {lead['Email']}") | |
st.write(f"**Phone:** {lead['Phone']}") | |
st.write(f"**Name:** {lead['First Name']} {lead['Last Name']}") | |
st.write(f"**Company:** {lead['Company']}") | |
st.write(f"**Job Title:** {lead['Job Title']}") | |
with col2: | |
st.write(f"**Search Term:** {lead['Search Term']}") | |
st.write(f"**Source URL:** {lead['Source URL']}") | |
st.write(f"**Page Title:** {lead['Page Title']}") | |
st.write(f"**Meta Description:** {lead['Meta Description']}") | |
st.write(f"**Phone Numbers:** {lead['Phone Numbers']}") | |
# Handle different possible formats of the Tags column | |
if isinstance(lead['Tags'], str): | |
try: | |
tags = json.loads(lead['Tags']) | |
st.write(f"**Tags:** {', '.join(tags)}") | |
except json.JSONDecodeError: | |
st.write(f"**Tags:** {lead['Tags']}") | |
elif isinstance(lead['Tags'], list): | |
st.write(f"**Tags:** {', '.join(lead['Tags'])}") | |
else: | |
st.write(f"**Tags:** {lead['Tags']}") | |
st.write("**Page Content:**") | |
if lead['Content'] is not None: | |
st.text(lead['Content'][:500] + "..." if len(lead['Content']) > 500 else lead['Content']) | |
else: | |
st.text("No content available") | |
# Optional: Add a download button for the full dataset | |
csv = st.session_state.leads.to_csv(index=False).encode('utf-8') | |
st.download_button( | |
label="Download full dataset as CSV", | |
data=csv, | |
file_name="leads_data.csv", | |
mime="text/csv", | |
) | |
def search_terms(): | |
st.header("Search Terms") | |
# Add a selector for different views | |
view_option = st.radio("Select View", ["Summary", "Basic List", "Search Term Groups"]) | |
if view_option == "Summary": | |
display_search_term_summary() | |
elif view_option == "Basic List": | |
display_basic_search_terms_list() | |
else: | |
display_search_term_groups() | |
def display_search_term_summary(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM search_terms") | |
total_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_terms WHERE group_id IS NOT NULL") | |
grouped_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_term_groups") | |
total_groups = cursor.fetchone()[0] | |
conn.close() | |
st.subheader("Search Terms Summary") | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Search Terms", total_terms) | |
col2.metric("Grouped Terms", grouped_terms) | |
col3.metric("Total Groups", total_groups) | |
st.subheader("Search Term Groups") | |
groups_data = fetch_search_term_groups_data() | |
sort_option = st.selectbox("Sort groups by", ["Name", "Number of Terms"]) | |
reverse_sort = st.checkbox("Reverse sort order") | |
if sort_option == "Name": | |
groups_data.sort(key=lambda x: x['group_name'], reverse=reverse_sort) | |
else: | |
groups_data.sort(key=lambda x: x['term_count'], reverse=reverse_sort) | |
for group in groups_data: | |
with st.expander(f"{group['group_name']} (Terms: {group['term_count']}, Leads: {group['total_leads']})"): | |
st.write(f"Description: {group['description']}") | |
st.write("Search Terms:") | |
terms_df = pd.DataFrame(group['terms']) | |
st.dataframe(terms_df) | |
st.header("Search Terms") | |
# Add a selector for different views | |
view_option = st.radio("Select View", ["Basic List", "Search Term Groups"]) | |
# Display statistics | |
display_search_term_statistics() | |
if view_option == "Basic List": | |
display_basic_search_terms_list() | |
else: | |
display_search_term_groups() | |
def display_search_term_statistics(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM search_terms") | |
total_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_terms WHERE group_id IS NOT NULL") | |
grouped_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_term_groups") | |
total_groups = cursor.fetchone()[0] | |
conn.close() | |
st.subheader("Search Terms Summary") | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Search Terms", total_terms) | |
col2.metric("Grouped Terms", grouped_terms) | |
col3.metric("Total Groups", total_groups) | |
# Pruning | |
if st.button("Prune Empty Terms"): | |
pruned_count = prune_empty_search_terms() | |
st.success(f"Pruned {pruned_count} search terms with no associated leads.") | |
# Undo prune | |
if 'last_pruned' in st.session_state and st.session_state.last_pruned > 0: | |
if st.button("Undo Last Prune"): | |
undo_last_prune() | |
st.success(f"Undone the last prune of {st.session_state.last_pruned} search terms.") | |
del st.session_state.last_pruned | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM search_terms") | |
total_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_terms WHERE group_id IS NOT NULL") | |
grouped_terms = cursor.fetchone()[0] | |
cursor.execute("SELECT COUNT(*) FROM search_term_groups") | |
total_groups = cursor.fetchone()[0] | |
conn.close() | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Search Terms", total_terms) | |
col2.metric("Grouped Terms", grouped_terms) | |
col3.metric("Total Groups", total_groups) | |
if st.button("Prune Empty Terms"): | |
pruned_count = prune_empty_search_terms() | |
st.success(f"Pruned {pruned_count} search terms with no associated leads.") | |
def prune_empty_search_terms(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
DELETE FROM search_terms | |
WHERE id NOT IN ( | |
SELECT DISTINCT search_term_id | |
FROM lead_sources | |
) | |
''') | |
pruned_count = cursor.rowcount | |
conn.commit() | |
conn.close() | |
return pruned_count | |
def undo_last_prune(): | |
# Implement undo logic for pruned terms | |
pass | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
DELETE FROM search_terms | |
WHERE id NOT IN ( | |
SELECT DISTINCT search_term_id | |
FROM lead_sources | |
) | |
''') | |
pruned_count = cursor.rowcount | |
conn.commit() | |
conn.close() | |
return pruned_count | |
if 'last_pruned' in st.session_state and st.session_state.last_pruned > 0: | |
if st.button("Undo Last Prune"): | |
undo_last_prune() | |
st.success(f"Undone the last prune of {st.session_state.last_pruned} search terms.") | |
del st.session_state.last_pruned | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
DELETE FROM search_terms | |
WHERE id NOT IN ( | |
SELECT DISTINCT search_term_id | |
FROM lead_sources | |
) | |
""") | |
pruned_count = cursor.rowcount | |
conn.commit() | |
conn.close() | |
return pruned_count | |
def display_basic_search_terms_list(): | |
col1, col2 = st.columns(2) | |
with col1: | |
st.subheader("Add Search Term") | |
with st.form(key="add_search_term_form"): | |
search_term = st.text_input("Search Term") | |
campaign_id = st.selectbox("Select Campaign", options=fetch_campaigns()) | |
submit_button = st.form_submit_button(label="Add Search Term") | |
if submit_button: | |
term_id = add_search_term(search_term, campaign_id.split(":")[0]) | |
st.success(f"Search term added with ID: {term_id}") | |
st.session_state.search_terms = fetch_search_terms() | |
with col2: | |
st.subheader("Existing Search Terms") | |
if st.button("Refresh Search Terms"): | |
st.session_state.search_terms = fetch_search_terms() | |
if 'search_terms' not in st.session_state: | |
st.session_state.search_terms = fetch_search_terms() | |
# Sort the dataframe by 'Leads Fetched' in descending order | |
sorted_terms = st.session_state.search_terms.sort_values('Leads Fetched', ascending=False) | |
for index, row in sorted_terms.iterrows(): | |
col1, col2 = st.columns([3, 1]) | |
with col1: | |
st.write(f"{row['ID']}: {row['Search Term']} (Leads: {row['Leads Fetched']})") | |
with col2: | |
if st.button("Delete", key=f"delete_{row['ID']}"): | |
st.session_state.confirm_delete = row['ID'] | |
st.session_state.leads_to_delete = delete_search_term_and_leads(row['ID']) | |
if 'confirm_delete' in st.session_state: | |
st.warning(f"Are you sure you want to delete search term {st.session_state.confirm_delete} and its related leads?") | |
st.write("Leads to be deleted:") | |
for lead in st.session_state.leads_to_delete: | |
st.write(f"- {lead[1]}") | |
col1, col2 = st.columns(2) | |
with col1: | |
if st.button("Confirm Delete"): | |
delete_search_term_and_leads(st.session_state.confirm_delete) | |
del st.session_state.confirm_delete | |
del st.session_state.leads_to_delete | |
st.session_state.search_terms = fetch_search_terms() | |
st.success("Search term and related leads deleted successfully.") | |
with col2: | |
if st.button("Cancel"): | |
del st.session_state.confirm_delete | |
del st.session_state.leads_to_delete | |
def display_search_term_groups(): | |
st.subheader("Search Term Groups") | |
# Fetch search term groups data | |
groups_data = fetch_search_term_groups_data() | |
# Sort options | |
sort_option = st.selectbox("Sort groups by", ["Name", "Number of Terms"]) | |
reverse_sort = st.checkbox("Reverse sort order") | |
if sort_option == "Name": | |
groups_data.sort(key=lambda x: x['group_name'], reverse=reverse_sort) | |
else: | |
groups_data.sort(key=lambda x: x['term_count'], reverse=reverse_sort) | |
# Display groups and their terms | |
for group in groups_data: | |
with st.expander(f"{group['group_name']} (Terms: {group['term_count']}, Leads: {group['total_leads']})"): | |
st.write(f"Description: {group['description']}") | |
st.write("Search Terms:") | |
terms_df = pd.DataFrame(group['terms']) | |
# Allow sorting | |
sort_column = st.selectbox(f"Sort by (Group {group['id']})", terms_df.columns) | |
sort_order = st.radio(f"Order (Group {group['id']})", ["Ascending", "Descending"]) | |
sorted_df = terms_df.sort_values(sort_column, ascending=(sort_order == "Ascending")) | |
st.dataframe(sorted_df) | |
# Delete group button | |
if st.button(f"Delete Group {group['id']}"): | |
delete_search_term_group(group['id']) | |
st.success(f"Group {group['id']} deleted successfully.") | |
st.rerun() | |
# Multi-select and assign to group | |
st.subheader("Assign Terms to Group") | |
all_terms = fetch_all_search_terms() | |
selected_terms = st.multiselect("Select terms to assign", all_terms) | |
new_group_name = st.text_input("New group name (leave empty to use existing)") | |
existing_groups = [g['group_name'] for g in groups_data] | |
target_group = st.selectbox("Select target group", [""] + existing_groups) | |
if st.button("Assign to Group"): | |
if new_group_name: | |
group_id = create_search_term_group(new_group_name) | |
elif target_group: | |
group_id = next(g['id'] for g in groups_data if g['group_name'] == target_group) | |
else: | |
st.error("Please provide a new group name or select an existing group.") | |
return | |
assign_terms_to_group(selected_terms, group_id) | |
st.success(f"Assigned {len(selected_terms)} terms to the group.") | |
st.rerun() | |
def message_templates(): | |
st.header("Message Templates") | |
col1, col2 = st.columns(2) | |
with col1: | |
st.subheader("Add Message Template") | |
with st.form(key="add_message_template_form"): | |
template_name = st.text_input("Template Name") | |
subject = st.text_input("Subject") | |
body_content = st.text_area("Body Content (HTML)", height=400) | |
campaign_id = st.selectbox("Select Campaign", options=fetch_campaigns()) | |
submit_button = st.form_submit_button(label="Add Message Template") | |
if submit_button: | |
template_id = create_message_template(template_name, subject, body_content, campaign_id.split(":")[0]) | |
st.success(f"Message template added with ID: {template_id}") | |
with col2: | |
st.subheader("Existing Message Templates") | |
if st.button("Refresh Message Templates"): | |
st.session_state.message_templates = fetch_message_templates() | |
if 'message_templates' not in st.session_state: | |
st.session_state.message_templates = fetch_message_templates() | |
st.dataframe(pd.DataFrame(st.session_state.message_templates, columns=["Template"]), use_container_width=True) | |
def view_sent_messages(): | |
st.header("View Sent Messages") | |
if st.button("Refresh Sent Messages"): | |
st.session_state.sent_messages = fetch_sent_messages() | |
if 'sent_messages' not in st.session_state: | |
st.session_state.sent_messages = fetch_sent_messages() | |
# Display messages in a more organized manner | |
for _, row in st.session_state.sent_messages.iterrows(): | |
with st.expander(f"Message to {row['Email']} - {row['Sent At']}"): | |
st.write(f"**Subject:** {row['Subject']}") | |
st.write(f"**Template:** {row['Template']}") | |
st.write(f"**Status:** {row['Status']}") | |
st.write(f"**Message ID:** {row['Message ID']}") | |
st.write("**Content:**") | |
st.markdown(row['Content'], unsafe_allow_html=True) | |
# Display summary statistics | |
st.subheader("Summary Statistics") | |
total_messages = len(st.session_state.sent_messages) | |
sent_messages = len(st.session_state.sent_messages[st.session_state.sent_messages['Status'] == 'sent']) | |
failed_messages = len(st.session_state.sent_messages[st.session_state.sent_messages['Status'] == 'failed']) | |
col1, col2, col3 = st.columns(3) | |
col1.metric("Total Messages", total_messages) | |
col2.metric("Sent Messages", sent_messages) | |
col3.metric("Failed Messages", failed_messages) | |
def projects_and_campaigns(): | |
st.header("Projects & Campaigns") | |
col1, col2 = st.columns(2) | |
with col1: | |
st.subheader("Add Project") | |
with st.form(key="add_project_form"): | |
project_name = st.text_input("Project Name") | |
submit_button = st.form_submit_button(label="Add Project") | |
if submit_button: | |
project_id = create_project(project_name) | |
st.success(f"Project added with ID: {project_id}") | |
with col2: | |
st.subheader("Add Campaign") | |
with st.form(key="add_campaign_form"): | |
campaign_name = st.text_input("Campaign Name") | |
project_id = st.selectbox("Select Project", options=fetch_projects()) | |
campaign_type = st.selectbox("Campaign Type", options=["Email", "SMS"]) | |
submit_button = st.form_submit_button(label="Add Campaign") | |
if submit_button: | |
campaign_id = create_campaign(campaign_name, project_id.split(":")[0], campaign_type) | |
st.success(f"Campaign added with ID: {campaign_id}") | |
def get_last_5_search_terms(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
SELECT term FROM search_terms | |
ORDER BY id DESC | |
LIMIT 5 | |
""") | |
terms = [row[0] for row in cursor.fetchall()] | |
conn.close() | |
return terms | |
def fetch_leads(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(''' | |
SELECT l.id, l.email, l.phone, l.first_name, l.last_name, l.company, l.job_title, | |
st.term as search_term, ls.url as source_url, ls.page_title, ls.meta_description, | |
ls.phone_numbers, ls.content, ls.tags | |
FROM leads l | |
LEFT JOIN lead_sources ls ON l.id = ls.lead_id | |
LEFT JOIN search_terms st ON ls.search_term_id = st.id | |
ORDER BY l.id DESC | |
''') | |
rows = cursor.fetchall() | |
conn.close() | |
return pd.DataFrame(rows, columns=["ID", "Email", "Phone", "First Name", "Last Name", "Company", "Job Title", | |
"Search Term", "Source URL", "Page Title", "Meta Description", | |
"Phone Numbers", "Content", "Tags"]) | |
import asyncio | |
import logging | |
from datetime import datetime | |
import time | |
import time | |
def automation_view(): | |
st.header("Automation Control Panel") | |
# Generate a unique key based on the current timestamp | |
unique_key = f"automation_{int(time.time() * 1000)}" | |
# Initialize automation_status if it doesn't exist | |
if 'automation_status' not in st.session_state: | |
st.session_state.automation_status = False | |
col1, col2 = st.columns(2) | |
with col1: | |
# Start/Stop Automation button | |
button_text = "Stop Automation" if st.session_state.automation_status else "Start Automation" | |
button_color = "secondary" if st.session_state.automation_status else "primary" | |
if st.button(button_text, key=f"{unique_key}_toggle", type=button_color): | |
st.session_state.automation_status = not st.session_state.automation_status | |
with col2: | |
# Quick Scan button | |
if st.button("Quick Scan", key=f"{unique_key}_scan", type="primary"): | |
with st.spinner("Performing quick scan..."): | |
# Implement quick scan logic here | |
st.success("Quick scan completed!") | |
# Display current automation status | |
if st.session_state.automation_status: | |
st.success("Automation is currently **ON**.") | |
else: | |
st.warning("Automation is currently **OFF**.") | |
# Real-time analytics for automation process | |
display_real_time_analytics() | |
# Additional control buttons | |
col1, col2, col3 = st.columns(3) | |
with col1: | |
if st.button("Optimize Search Terms", key=f"{unique_key}_optimize", type="secondary"): | |
with st.spinner("Optimizing search terms..."): | |
asyncio.run(continuous_search_term_optimization()) | |
st.success("Search terms optimized.") | |
with col2: | |
if st.button("Send Test Email", key=f"{unique_key}_test_email", type="secondary"): | |
with st.spinner("Sending test email..."): | |
# Implement test email sending logic here | |
st.success("Test email sent successfully.") | |
with col3: | |
if st.button("Generate Report", key=f"{unique_key}_report", type="secondary"): | |
with st.spinner("Generating report..."): | |
# Implement report generation logic here | |
st.success("Report generated successfully.") | |
# Run the automation process if it's ON | |
if st.session_state.automation_status: | |
if st.button("Run Full Automation Cycle", key=f"{unique_key}_run", type="primary"): | |
with st.spinner("Running full automation cycle..."): | |
asyncio.run(continuous_automation_process()) | |
st.success("Full automation cycle completed.") | |
# ... (rest of the code remains unchanged) | |
# Separate function for the continuous process | |
async def continuous_automation_process(): | |
try: | |
# Step 1: Gather leads | |
st.write("Gathering leads...") | |
await continuous_lead_collection() | |
# Step 2: Optimize search terms | |
st.write("Optimizing search terms...") | |
await continuous_search_term_optimization() | |
# Step 3: Send emails | |
st.write("Sending emails...") | |
await continuous_email_sending() | |
except Exception as e: | |
st.error(f"Error during automation process: {e}") | |
logging.error(f"Error during automation process: {e}") | |
# Make sure to add this function if it doesn't exist: | |
def display_real_time_analytics(): | |
st.subheader("Real-Time Analytics") | |
total_leads = count_total_leads() | |
leads_last_24_hours = count_leads_last_24_hours() | |
emails_sent = count_emails_sent() | |
optimized_terms = count_optimized_search_terms() | |
col1, col2, col3, col4 = st.columns(4) | |
col1.metric("Total Leads", total_leads) | |
col2.metric("Leads in Last 24 Hours", leads_last_24_hours) | |
col3.metric("Emails Sent", emails_sent) | |
col4.metric("Optimized Terms", optimized_terms) | |
# Count total leads gathered | |
def count_total_leads(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM leads") | |
result = cursor.fetchone()[0] | |
conn.close() | |
return result | |
# Count leads gathered in the last 24 hours | |
def count_leads_last_24_hours(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM leads WHERE created_at >= datetime('now', '-1 day')") | |
result = cursor.fetchone()[0] | |
conn.close() | |
return result | |
# Count total emails sent | |
def count_emails_sent(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM messages WHERE status = 'sent'") | |
result = cursor.fetchone()[0] | |
conn.close() | |
return result | |
# Count the number of optimized search terms | |
def count_optimized_search_terms(): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute("SELECT COUNT(*) FROM optimized_search_terms") | |
result = cursor.fetchone()[0] | |
conn.close() | |
return result | |
# Continuously run lead gathering, email sending, and search term optimization | |
async def continuous_automation_process(): | |
while st.session_state.automation_status: | |
try: | |
# Step 1: Gather leads | |
st.write(f"{datetime.now()}: Gathering leads...") | |
await continuous_lead_collection() | |
# Step 2: Optimize search terms | |
st.write(f"{datetime.now()}: Optimizing search terms...") | |
await continuous_search_term_optimization() | |
# Step 3: Send emails | |
st.write(f"{datetime.now()}: Sending emails...") | |
await continuous_email_sending() | |
# Wait between iterations | |
await asyncio.sleep(60) # Adjust the sleep interval as necessary (e.g., 60 seconds) | |
except Exception as e: | |
logging.error(f"Error during automation process: {e}") | |
break | |
# Step 1: Lead gathering automation | |
async def continuous_lead_collection(num_results_per_term=10, sleep_interval=60): | |
"""Automate lead collection process.""" | |
search_terms = get_least_searched_terms(5) # Get least searched terms for optimization | |
for term in search_terms: | |
campaign_id = 1 # Default campaign | |
results = await asyncio.to_thread(manual_search_wrapper, term, num_results_per_term, campaign_id) | |
for result in results: | |
email = result[0] | |
if is_valid_email(email): | |
lead_id = save_lead(email, None, None, None, None, None) | |
add_lead_to_campaign(campaign_id, lead_id) | |
# Wait for the next iteration | |
await asyncio.sleep(sleep_interval) | |
# Step 2: Search term optimization automation | |
async def continuous_search_term_optimization(): | |
"""Automate search term optimization.""" | |
current_terms = fetch_all_search_terms() # Fetch all current terms | |
kb_info = get_knowledge_base_info() # Get context from knowledge base | |
# Optimize search terms with AI | |
optimized_terms = optimize_search_terms(current_terms, kb_info) | |
# Save optimized terms | |
save_optimized_search_terms(optimized_terms) | |
log_ai_request("Optimize Search Terms", current_terms, optimized_terms) | |
# Step 3: Email sending automation | |
async def continuous_email_sending(sleep_interval=60): | |
"""Automate bulk email sending.""" | |
campaign_id = 1 # Example campaign | |
leads = fetch_leads_for_bulk_send(template_id="1", send_option="All Not Contacted", filter_option="Filter Out blog-directory") | |
if leads: | |
logs = await bulk_send("1", "[email protected]", "[email protected]", leads) | |
for log in logs: | |
logging.info(log) | |
# Wait for the next iteration | |
await asyncio.sleep(sleep_interval) | |
# AI-powered search term optimization | |
def optimize_search_terms(current_terms, kb_info): | |
prompt = f""" | |
Optimize the following search terms for high-quality lead generation: | |
Search Terms: {', '.join(current_terms)} | |
Knowledge Base Info: {kb_info} | |
Focus on terms that will attract leads with high conversion potential and align with the product. | |
""" | |
response = client.chat.completions.create( | |
model="gpt-4", messages=[{"role": "user", "content": prompt}] | |
) | |
log_ai_request("Optimize Search Terms", prompt, response.choices[0].message.content) | |
return response.choices[0].message.content.split('\n') | |
# Save AI request logs | |
def log_ai_request(request_type, prompt, response): | |
log_entry = f"{datetime.now()}: {request_type}\nPrompt: {prompt}\nResponse: {response}" | |
ai_request_logs.append(log_entry) | |
logging.info(log_entry) | |
# Save optimized search terms | |
def save_optimized_search_terms(optimized_terms): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
for term in optimized_terms: | |
cursor.execute("INSERT INTO optimized_search_terms (term) VALUES (?)", (term,)) | |
conn.commit() | |
conn.close() | |
async def supervisor_check(): | |
ineffective_terms = get_ineffective_search_terms(threshold=0.3) # Terms with <30% lead success | |
for term in ineffective_terms: | |
optimized_term = refine_search_term(term) | |
update_search_term(term, optimized_term) | |
def get_ineffective_search_terms(threshold): | |
conn = get_db_connection() | |
cursor = conn.cursor() | |
cursor.execute(""" | |
SELECT term FROM search_term_effectiveness | |
WHERE (valid_leads / total_results) < ? | |
""", (threshold,)) | |
terms = cursor.fetchall() | |
conn.close() | |
return [term[0] for term in terms] | |
def chatbot_style_logs(logs): | |
st.subheader("Automation Logs") | |
for log in logs: | |
st.markdown(f"**[{log['timestamp']}]** {log['message']}") | |
def log_ai_request(request_type, prompt, response): | |
log_entry = {"timestamp": datetime.now(), "message": f"{request_type}: {response}"} | |
ai_request_logs.append(log_entry) | |
logging.info(f"{request_type}: {response}") | |
chatbot_style_logs(ai_request_logs) | |
def automation_control_panel(): | |
st.header("Automation Control") | |
# Toggle automation ON/OFF | |
if 'automation_status' not in st.session_state: | |
st.session_state.automation_status = False | |
if st.button("Toggle Automation"): | |
st.session_state.automation_status = not st.session_state.automation_status | |
if st.session_state.automation_status: | |
st.write("**Automation is currently ON.**") | |
asyncio.run(continuous_automation_process()) | |
else: | |
st.write("**Automation is currently OFF.**") | |
# Real-time analytics | |
display_real_time_analytics() | |
def display_real_time_analytics(): | |
total_leads = count_total_leads() | |
leads_last_24h = count_leads_last_24_hours() | |
emails_sent = count_emails_sent() | |
optimized_terms = count_optimized_search_terms() | |
col1, col2, col3, col4 = st.columns(4) | |
col1.metric("Total Leads", total_leads) | |
col2.metric("Leads in Last 24 Hours", leads_last_24h) | |
col3.metric("Emails Sent", emails_sent) | |
col4.metric("Optimized Terms", optimized_terms) | |
async def continuous_automation_process(): | |
cycle_count = 0 | |
while st.session_state.automation_status: | |
try: | |
# Lead collection step | |
await continuous_lead_collection() | |
# Search term optimization every 5 cycles | |
if cycle_count % 5 == 0: | |
supervisor_check() | |
# Email sending step | |
await continuous_email_sending() | |
cycle_count += 1 | |
await asyncio.sleep(60) # Run the cycle every 60 seconds | |
except Exception as e: | |
logging.error(f"Error in automation: {e}") | |
st.error(f"Error in automation process: {e}") | |
# Initialize real-time analytics in Streamlit | |
if __name__ == "__main__": | |
st.title("AUTOCLIENT - Full Automation System") | |
# Automation view | |
automation_view() | |
# Start the continuous automation process if enabled | |
if st.session_state.automation_status: | |
asyncio.run(continuous_automation_process()) | |
# Call the main function to run the Streamlit app | |
if __name__ == "__main__": | |
main() | |