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Runtime error
Runtime error
Create backupapp2.py
Browse files- backupapp2.py +740 -0
backupapp2.py
ADDED
@@ -0,0 +1,740 @@
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1 |
+
import streamlit as st
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2 |
+
import streamlit.components.v1 as components
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3 |
+
import os
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4 |
+
import base64
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5 |
+
import glob
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6 |
+
import io
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7 |
+
import json
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8 |
+
import mistune
|
9 |
+
import pytz
|
10 |
+
import math
|
11 |
+
import requests
|
12 |
+
import sys
|
13 |
+
import time
|
14 |
+
import re
|
15 |
+
import textract
|
16 |
+
import zipfile
|
17 |
+
import random
|
18 |
+
import httpx # add 11/13/23
|
19 |
+
import asyncio
|
20 |
+
from openai import OpenAI
|
21 |
+
#from openai import AsyncOpenAI
|
22 |
+
from datetime import datetime
|
23 |
+
from xml.etree import ElementTree as ET
|
24 |
+
from bs4 import BeautifulSoup
|
25 |
+
from collections import deque
|
26 |
+
from audio_recorder_streamlit import audio_recorder
|
27 |
+
from dotenv import load_dotenv
|
28 |
+
from PyPDF2 import PdfReader
|
29 |
+
from langchain.text_splitter import CharacterTextSplitter
|
30 |
+
from langchain.embeddings import OpenAIEmbeddings
|
31 |
+
from langchain.vectorstores import FAISS
|
32 |
+
from langchain.chat_models import ChatOpenAI
|
33 |
+
from langchain.memory import ConversationBufferMemory
|
34 |
+
from langchain.chains import ConversationalRetrievalChain
|
35 |
+
from templates import css, bot_template, user_template
|
36 |
+
from io import BytesIO
|
37 |
+
from contextlib import redirect_stdout
|
38 |
+
# code import tests
|
39 |
+
import seaborn
|
40 |
+
import plotly
|
41 |
+
import vega_datasets
|
42 |
+
import bokeh
|
43 |
+
import holoviews
|
44 |
+
import plotnine
|
45 |
+
import graphviz
|
46 |
+
import tensorflow
|
47 |
+
import torch
|
48 |
+
|
49 |
+
# set page config once
|
50 |
+
st.set_page_config(page_title="Python AI Pair Programmer", layout="wide")
|
51 |
+
|
52 |
+
# UI for sidebar controls
|
53 |
+
should_save = st.sidebar.checkbox("πΎ Save", value=True)
|
54 |
+
col1, col2, col3, col4 = st.columns(4)
|
55 |
+
with col1:
|
56 |
+
with st.expander("Settings π§ πΎ", expanded=True):
|
57 |
+
# File type for output, model choice
|
58 |
+
menu = ["txt", "htm", "xlsx", "csv", "md", "py"]
|
59 |
+
choice = st.sidebar.selectbox("Output File Type:", menu)
|
60 |
+
model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301'))
|
61 |
+
|
62 |
+
# Define a context dictionary to maintain the state between exec calls
|
63 |
+
context = {}
|
64 |
+
|
65 |
+
def create_file(filename, prompt, response, should_save=True):
|
66 |
+
if not should_save:
|
67 |
+
return
|
68 |
+
|
69 |
+
# Extract base filename without extension
|
70 |
+
base_filename, ext = os.path.splitext(filename)
|
71 |
+
|
72 |
+
# Initialize the combined content
|
73 |
+
combined_content = ""
|
74 |
+
|
75 |
+
# Add Prompt with markdown title and emoji
|
76 |
+
combined_content += "# Prompt π\n" + prompt + "\n\n"
|
77 |
+
|
78 |
+
# Add Response with markdown title and emoji
|
79 |
+
combined_content += "# Response π¬\n" + response + "\n\n"
|
80 |
+
|
81 |
+
# Check for code blocks in the response
|
82 |
+
resources = re.findall(r"```([\s\S]*?)```", response)
|
83 |
+
for resource in resources:
|
84 |
+
# Check if the resource contains Python code
|
85 |
+
if "python" in resource.lower():
|
86 |
+
# Remove the 'python' keyword from the code block
|
87 |
+
cleaned_code = re.sub(r'^\s*python', '', resource, flags=re.IGNORECASE | re.MULTILINE)
|
88 |
+
|
89 |
+
# Add Code Results title with markdown and emoji
|
90 |
+
combined_content += "# Code Results π\n"
|
91 |
+
|
92 |
+
# Redirect standard output to capture it
|
93 |
+
original_stdout = sys.stdout
|
94 |
+
sys.stdout = io.StringIO()
|
95 |
+
|
96 |
+
# Execute the cleaned Python code within the context
|
97 |
+
try:
|
98 |
+
exec(cleaned_code, context)
|
99 |
+
code_output = sys.stdout.getvalue()
|
100 |
+
combined_content += f"```\n{code_output}\n```\n\n"
|
101 |
+
realtimeEvalResponse = "# Code Results π\n" + "```" + code_output + "```\n\n"
|
102 |
+
st.code(realtimeEvalResponse)
|
103 |
+
|
104 |
+
except Exception as e:
|
105 |
+
combined_content += f"```python\nError executing Python code: {e}\n```\n\n"
|
106 |
+
|
107 |
+
# Restore the original standard output
|
108 |
+
sys.stdout = original_stdout
|
109 |
+
else:
|
110 |
+
# Add non-Python resources with markdown and emoji
|
111 |
+
combined_content += "# Resource π οΈ\n" + "```" + resource + "```\n\n"
|
112 |
+
|
113 |
+
# Save the combined content to a Markdown file
|
114 |
+
if should_save:
|
115 |
+
with open(f"{base_filename}.md", 'w') as file:
|
116 |
+
file.write(combined_content)
|
117 |
+
st.code(combined_content)
|
118 |
+
|
119 |
+
# Create a Base64 encoded link for the file
|
120 |
+
with open(f"{base_filename}.md", 'rb') as file:
|
121 |
+
encoded_file = base64.b64encode(file.read()).decode()
|
122 |
+
href = f'<a href="data:file/markdown;base64,{encoded_file}" download="{filename}">Download File π</a>'
|
123 |
+
st.markdown(href, unsafe_allow_html=True)
|
124 |
+
|
125 |
+
|
126 |
+
# Read it aloud
|
127 |
+
def readitaloud(result):
|
128 |
+
documentHTML5='''
|
129 |
+
<!DOCTYPE html>
|
130 |
+
<html>
|
131 |
+
<head>
|
132 |
+
<title>Read It Aloud</title>
|
133 |
+
<script type="text/javascript">
|
134 |
+
function readAloud() {
|
135 |
+
const text = document.getElementById("textArea").value;
|
136 |
+
const speech = new SpeechSynthesisUtterance(text);
|
137 |
+
window.speechSynthesis.speak(speech);
|
138 |
+
}
|
139 |
+
</script>
|
140 |
+
</head>
|
141 |
+
<body>
|
142 |
+
<h1>π Read It Aloud</h1>
|
143 |
+
<textarea id="textArea" rows="10" cols="80">
|
144 |
+
'''
|
145 |
+
documentHTML5 = documentHTML5 + result
|
146 |
+
documentHTML5 = documentHTML5 + '''
|
147 |
+
</textarea>
|
148 |
+
<br>
|
149 |
+
<button onclick="readAloud()">π Read Aloud</button>
|
150 |
+
</body>
|
151 |
+
</html>
|
152 |
+
'''
|
153 |
+
|
154 |
+
components.html(documentHTML5, width=800, height=300)
|
155 |
+
#return result
|
156 |
+
|
157 |
+
def generate_filename(prompt, file_type):
|
158 |
+
central = pytz.timezone('US/Central')
|
159 |
+
safe_date_time = datetime.now(central).strftime("%m%d_%H%M")
|
160 |
+
replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")
|
161 |
+
safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]
|
162 |
+
return f"{safe_date_time}_{safe_prompt}.{file_type}"
|
163 |
+
|
164 |
+
# Chat and Chat with files
|
165 |
+
def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
|
166 |
+
model = model_choice
|
167 |
+
conversation = [{'role': 'system', 'content': 'You are a python script writer.'}]
|
168 |
+
conversation.append({'role': 'user', 'content': prompt})
|
169 |
+
if len(document_section)>0:
|
170 |
+
conversation.append({'role': 'assistant', 'content': document_section})
|
171 |
+
start_time = time.time()
|
172 |
+
report = []
|
173 |
+
res_box = st.empty()
|
174 |
+
collected_chunks = []
|
175 |
+
collected_messages = []
|
176 |
+
key = os.getenv('OPENAI_API_KEY')
|
177 |
+
|
178 |
+
client = OpenAI(
|
179 |
+
api_key= os.getenv('OPENAI_API_KEY')
|
180 |
+
)
|
181 |
+
stream = client.chat.completions.create(
|
182 |
+
model='gpt-3.5-turbo',
|
183 |
+
messages=conversation,
|
184 |
+
stream=True,
|
185 |
+
)
|
186 |
+
all_content = "" # Initialize an empty string to hold all content
|
187 |
+
for part in stream:
|
188 |
+
chunk_message = (part.choices[0].delta.content or "")
|
189 |
+
collected_messages.append(chunk_message) # save the message
|
190 |
+
content=part.choices[0].delta.content
|
191 |
+
try:
|
192 |
+
if len(content) > 0:
|
193 |
+
report.append(content)
|
194 |
+
all_content += content
|
195 |
+
result = "".join(report).strip()
|
196 |
+
res_box.markdown(f'*{result}*')
|
197 |
+
except:
|
198 |
+
st.write(' ')
|
199 |
+
full_reply_content = all_content
|
200 |
+
st.write("Elapsed time:")
|
201 |
+
st.write(time.time() - start_time)
|
202 |
+
filename = generate_filename(full_reply_content, choice)
|
203 |
+
create_file(filename, prompt, full_reply_content, should_save)
|
204 |
+
readitaloud(full_reply_content)
|
205 |
+
return full_reply_content
|
206 |
+
|
207 |
+
def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
|
208 |
+
conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
|
209 |
+
conversation.append({'role': 'user', 'content': prompt})
|
210 |
+
if len(file_content)>0:
|
211 |
+
conversation.append({'role': 'assistant', 'content': file_content})
|
212 |
+
client = OpenAI(
|
213 |
+
api_key= os.getenv('OPENAI_API_KEY')
|
214 |
+
)
|
215 |
+
response = client.chat.completions.create(model=model_choice, messages=conversation)
|
216 |
+
return response['choices'][0]['message']['content']
|
217 |
+
|
218 |
+
def link_button_with_emoji(url, title, emoji_summary):
|
219 |
+
emojis = ["π", "π₯", "π‘οΈ", "π©Ί", "π¬", "π", "π§ͺ", "π¨ββοΈ", "π©ββοΈ"]
|
220 |
+
random_emoji = random.choice(emojis)
|
221 |
+
st.markdown(f"[{random_emoji} {emoji_summary} - {title}]({url})")
|
222 |
+
|
223 |
+
python_parts = {
|
224 |
+
"Syntax": {"emoji": "βοΈ", "details": "Variables, Comments, Printing"},
|
225 |
+
"Data Types": {"emoji": "π", "details": "Numbers, Strings, Lists, Tuples, Sets, Dictionaries"},
|
226 |
+
"Control Structures": {"emoji": "π", "details": "If, Elif, Else, Loops, Break, Continue"},
|
227 |
+
"Functions": {"emoji": "π§", "details": "Defining, Calling, Parameters, Return Values"},
|
228 |
+
"Classes": {"emoji": "ποΈ", "details": "Creating, Inheritance, Methods, Properties"},
|
229 |
+
"API Interaction": {"emoji": "π", "details": "Requests, JSON Parsing, HTTP Methods"},
|
230 |
+
"Data Visualization Libraries1": {"emoji": "π", "details": "matplotlib"},
|
231 |
+
"Data Visualization Libraries2": {"emoji": "π", "details": "seaborn"},
|
232 |
+
"Data Visualization Libraries3": {"emoji": "π", "details": "plotly"},
|
233 |
+
"Data Visualization Libraries4": {"emoji": "π", "details": "altair"},
|
234 |
+
"Data Visualization Libraries5": {"emoji": "π", "details": "bokeh"},
|
235 |
+
"Data Visualization Libraries6": {"emoji": "π", "details": "pydeck"},
|
236 |
+
"Data Visualization Libraries7": {"emoji": "π", "details": "holoviews"},
|
237 |
+
"Data Visualization Libraries8": {"emoji": "π", "details": "plotnine"},
|
238 |
+
"Data Visualization Libraries9": {"emoji": "π", "details": "graphviz"},
|
239 |
+
"Error Handling": {"emoji": "β οΈ", "details": "Try, Except, Finally, Raising"},
|
240 |
+
"Scientific & Data Analysis Libraries": {"emoji": "π§ͺ", "details": "Numpy, Pandas, Scikit-Learn, TensorFlow, SciPy, Pillow"},
|
241 |
+
"Advanced Concepts": {"emoji": "π§ ", "details": "Decorators, Generators, Context Managers, Metaclasses, Asynchronous Programming"},
|
242 |
+
"Web & Network Libraries": {"emoji": "πΈοΈ", "details": "Flask, Django, Requests, BeautifulSoup, HTTPX, Asyncio"},
|
243 |
+
"Streamlit & Extensions1": {"emoji": "π‘", "details": "Streamlit"},
|
244 |
+
"Streamlit & Extensions2": {"emoji": "π‘", "details": "Streamlit-AgGrid"},
|
245 |
+
"Streamlit & Extensions3": {"emoji": "π‘", "details": "Streamlit-Folium"},
|
246 |
+
"Streamlit & Extensions4": {"emoji": "π‘", "details": "Streamlit-Pandas-Profiling"},
|
247 |
+
"Streamlit & Extensions5": {"emoji": "π‘", "details": "Streamlit-Vega-Lite, Gradio"},
|
248 |
+
"Gradio": {"emoji": "π‘", "details": "gradio"},
|
249 |
+
"File Handling & Serialization": {"emoji": "π", "details": "PyPDF2, Pytz, Json, Base64, Zipfile, Random, Glob, IO"},
|
250 |
+
"Machine Learning & AI": {"emoji": "π€", "details": "OpenAI, LangChain, HuggingFace"},
|
251 |
+
"Text & Data Extraction": {"emoji": "π", "details": "TikToken, Textract, SQLAlchemy, Pillow"},
|
252 |
+
"XML & Collections Libraries": {"emoji": "π", "details": "XML, Collections"},
|
253 |
+
"Top PyPI Libraries1": {"emoji": "π", "details": "Requests, Pillow, SQLAlchemy, Flask, Django, SciPy, Beautiful Soup, PyTest, PyGame, Twisted"},
|
254 |
+
"Top PyPI Libraries2": {"emoji": "π", "details": "numpy, pandas, matplotlib, requests, beautifulsoup4"},
|
255 |
+
"Top PyPI Libraries3": {"emoji": "π", "details": "langchain, openai, PyPDF2, pytz"},
|
256 |
+
"Top PyPI Libraries4": {"emoji": "π", "details": "streamlit, audio_recorder_streamlit, gradio"},
|
257 |
+
"Top PyPI Libraries5": {"emoji": "π", "details": "tiktoken, textract, glob, io"},
|
258 |
+
"Top PyPI Libraries6": {"emoji": "π", "details": "matplotlib, seaborn, plotly, altair, bokeh, pydeck"},
|
259 |
+
"Top PyPI Libraries7": {"emoji": "π", "details": "streamlit, streamlit-aggrid, streamlit-folium, streamlit-pandas-profiling, streamlit-vega-lite"},
|
260 |
+
"Top PyPI Libraries8": {"emoji": "π", "details": "holoviews, plotnine, graphviz"},
|
261 |
+
"Top PyPI Libraries9": {"emoji": "π", "details": "json, base64, zipfile, random"},
|
262 |
+
"Top PyPI Libraries10": {"emoji": "π", "details": "httpx, asyncio, xml, collections, huggingface "}
|
263 |
+
}
|
264 |
+
|
265 |
+
|
266 |
+
response_placeholders = {}
|
267 |
+
example_placeholders = {}
|
268 |
+
|
269 |
+
def display_python_parts_old2():
|
270 |
+
st.title("Python Interactive Learning Platform")
|
271 |
+
|
272 |
+
for part, content in python_parts.items():
|
273 |
+
with st.expander(f"{content['emoji']} {part} - {content['details']}", expanded=False):
|
274 |
+
if st.button(f"Show Example for {part}", key=f"example_{part}"):
|
275 |
+
example = "Write short python script examples with mock data in python list dictionary for inputs for " + part
|
276 |
+
example_placeholders[part] = example
|
277 |
+
st.code(example_placeholders[part], language="python")
|
278 |
+
response = chat_with_model(f'Write python script with short code examples for: {content["details"]}', part)
|
279 |
+
response_placeholders[part] = response
|
280 |
+
st.write(f"#### {content['emoji']} {part} Example")
|
281 |
+
st.code(response_placeholders[part], language="python")
|
282 |
+
|
283 |
+
if st.button(f"Take Quiz on {part}", key=f"quiz_{part}"):
|
284 |
+
quiz = "Write Python script quiz examples with mock static data inputs for " + part
|
285 |
+
response = chat_with_model(f'Write python code blocks for quiz program: {quiz}', part)
|
286 |
+
response_placeholders[part] = response
|
287 |
+
st.write(f"#### {content['emoji']} {part} Quiz")
|
288 |
+
st.code(response_placeholders[part], language="python")
|
289 |
+
|
290 |
+
prompt = f"Write python script with a few advanced coding examples using mock data input for {content['details']}"
|
291 |
+
if st.button(f"Explore {part}", key=part):
|
292 |
+
response = chat_with_model(prompt, part)
|
293 |
+
response_placeholders[part] = response
|
294 |
+
st.write(f"#### {content['emoji']} {part} Details")
|
295 |
+
st.code(response_placeholders[part], language="python")
|
296 |
+
|
297 |
+
|
298 |
+
def display_python_parts():
|
299 |
+
st.title("Python Interactive Learning Platform")
|
300 |
+
for part, content in python_parts.items():
|
301 |
+
with st.expander(f"{content['emoji']} {part} - {content['details']}", expanded=False):
|
302 |
+
if st.button(f"Show Example for {part}", key=f"example_{part}"):
|
303 |
+
example = "Python script example with mock example inputs for " + part
|
304 |
+
example_placeholders[part] = example
|
305 |
+
st.code(example_placeholders[part], language="python")
|
306 |
+
response = chat_with_model('Create detailed advanced python script code examples for:' + example_placeholders[part], part)
|
307 |
+
if st.button(f"Take Quiz on {part}", key=f"quiz_{part}"):
|
308 |
+
quiz = "Python script quiz example with mock example inputs for " + part
|
309 |
+
response = chat_with_model(quiz, part)
|
310 |
+
prompt = f"Learn about advanced coding examples using mock example inputs for {content['details']}"
|
311 |
+
if st.button(f"Explore {part}", key=part):
|
312 |
+
response = chat_with_model(prompt, part)
|
313 |
+
response_placeholders[part] = response
|
314 |
+
if part in response_placeholders:
|
315 |
+
st.markdown(f"**Response:** {response_placeholders[part]}")
|
316 |
+
|
317 |
+
def add_paper_buttons_and_links():
|
318 |
+
page = st.sidebar.radio("Choose a page:", ["Python Pair Programmer"])
|
319 |
+
if page == "Python Pair Programmer":
|
320 |
+
display_python_parts()
|
321 |
+
|
322 |
+
col1, col2, col3, col4 = st.columns(4)
|
323 |
+
|
324 |
+
with col1:
|
325 |
+
with st.expander("MemGPT π§ πΎ", expanded=False):
|
326 |
+
link_button_with_emoji("https://arxiv.org/abs/2310.08560", "MemGPT", "π§ πΎ Memory OS")
|
327 |
+
outline_memgpt = "Memory Hierarchy, Context Paging, Self-directed Memory Updates, Memory Editing, Memory Retrieval, Preprompt Instructions, Semantic Memory, Episodic Memory, Emotional Contextual Understanding"
|
328 |
+
if st.button("Discuss MemGPT Features"):
|
329 |
+
chat_with_model("Discuss the key features of MemGPT: " + outline_memgpt, "MemGPT")
|
330 |
+
|
331 |
+
with col2:
|
332 |
+
with st.expander("AutoGen π€π", expanded=False):
|
333 |
+
link_button_with_emoji("https://arxiv.org/abs/2308.08155", "AutoGen", "π€π Multi-Agent LLM")
|
334 |
+
outline_autogen = "Cooperative Conversations, Combining Capabilities, Complex Task Solving, Divergent Thinking, Factuality, Highly Capable Agents, Generic Abstraction, Effective Implementation"
|
335 |
+
if st.button("Explore AutoGen Multi-Agent LLM"):
|
336 |
+
chat_with_model("Explore the key features of AutoGen: " + outline_autogen, "AutoGen")
|
337 |
+
|
338 |
+
with col3:
|
339 |
+
with st.expander("Whisper ππ§βπ", expanded=False):
|
340 |
+
link_button_with_emoji("https://arxiv.org/abs/2212.04356", "Whisper", "ππ§βπ Robust STT")
|
341 |
+
outline_whisper = "Scaling, Deep Learning Approaches, Weak Supervision, Zero-shot Transfer Learning, Accuracy & Robustness, Pre-training Techniques, Broad Range of Environments, Combining Multiple Datasets"
|
342 |
+
if st.button("Learn About Whisper STT"):
|
343 |
+
chat_with_model("Learn about the key features of Whisper: " + outline_whisper, "Whisper")
|
344 |
+
|
345 |
+
with col4:
|
346 |
+
with st.expander("ChatDev π¬π»", expanded=False):
|
347 |
+
link_button_with_emoji("https://arxiv.org/pdf/2307.07924.pdf", "ChatDev", "π¬π» Comm. Agents")
|
348 |
+
outline_chatdev = "Effective Communication, Comprehensive Software Solutions, Diverse Social Identities, Tailored Codes, Environment Dependencies, User Manuals"
|
349 |
+
if st.button("Deep Dive into ChatDev"):
|
350 |
+
chat_with_model("Deep dive into the features of ChatDev: " + outline_chatdev, "ChatDev")
|
351 |
+
|
352 |
+
add_paper_buttons_and_links()
|
353 |
+
|
354 |
+
|
355 |
+
# Process user input is a post processor algorithm which runs after document embedding vector DB play of GPT on context of documents..
|
356 |
+
def process_user_input(user_question):
|
357 |
+
# Check and initialize 'conversation' in session state if not present
|
358 |
+
if 'conversation' not in st.session_state:
|
359 |
+
st.session_state.conversation = {} # Initialize with an empty dictionary or an appropriate default value
|
360 |
+
|
361 |
+
response = st.session_state.conversation({'question': user_question})
|
362 |
+
st.session_state.chat_history = response['chat_history']
|
363 |
+
|
364 |
+
for i, message in enumerate(st.session_state.chat_history):
|
365 |
+
template = user_template if i % 2 == 0 else bot_template
|
366 |
+
st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
|
367 |
+
|
368 |
+
# Save file output from PDF query results
|
369 |
+
filename = generate_filename(user_question, 'txt')
|
370 |
+
create_file(filename, user_question, message.content, should_save)
|
371 |
+
|
372 |
+
# New functionality to create expanders and buttons
|
373 |
+
create_expanders_and_buttons(message.content)
|
374 |
+
|
375 |
+
def create_expanders_and_buttons(content):
|
376 |
+
# Split the content into paragraphs
|
377 |
+
paragraphs = content.split("\n\n")
|
378 |
+
for paragraph in paragraphs:
|
379 |
+
# Identify the header and detail in the paragraph
|
380 |
+
header, detail = extract_feature_and_detail(paragraph)
|
381 |
+
if header and detail:
|
382 |
+
with st.expander(header, expanded=False):
|
383 |
+
if st.button(f"Explore {header}"):
|
384 |
+
expanded_outline = "Expand on the feature: " + detail
|
385 |
+
chat_with_model(expanded_outline, header)
|
386 |
+
|
387 |
+
def extract_feature_and_detail(paragraph):
|
388 |
+
# Use regex to find the header and detail in the paragraph
|
389 |
+
match = re.match(r"(.*?):(.*)", paragraph)
|
390 |
+
if match:
|
391 |
+
header = match.group(1).strip()
|
392 |
+
detail = match.group(2).strip()
|
393 |
+
return header, detail
|
394 |
+
return None, None
|
395 |
+
|
396 |
+
def transcribe_audio(file_path, model):
|
397 |
+
key = os.getenv('OPENAI_API_KEY')
|
398 |
+
headers = {
|
399 |
+
"Authorization": f"Bearer {key}",
|
400 |
+
}
|
401 |
+
with open(file_path, 'rb') as f:
|
402 |
+
data = {'file': f}
|
403 |
+
st.write("Read file {file_path}", file_path)
|
404 |
+
OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"
|
405 |
+
response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})
|
406 |
+
if response.status_code == 200:
|
407 |
+
st.write(response.json())
|
408 |
+
chatResponse = chat_with_model(response.json().get('text'), '') # *************************************
|
409 |
+
transcript = response.json().get('text')
|
410 |
+
#st.write('Responses:')
|
411 |
+
#st.write(chatResponse)
|
412 |
+
filename = generate_filename(transcript, 'txt')
|
413 |
+
#create_file(filename, transcript, chatResponse)
|
414 |
+
response = chatResponse
|
415 |
+
user_prompt = transcript
|
416 |
+
create_file(filename, user_prompt, response, should_save)
|
417 |
+
return transcript
|
418 |
+
else:
|
419 |
+
st.write(response.json())
|
420 |
+
st.error("Error in API call.")
|
421 |
+
return None
|
422 |
+
|
423 |
+
def save_and_play_audio(audio_recorder):
|
424 |
+
audio_bytes = audio_recorder()
|
425 |
+
if audio_bytes:
|
426 |
+
filename = generate_filename("Recording", "wav")
|
427 |
+
with open(filename, 'wb') as f:
|
428 |
+
f.write(audio_bytes)
|
429 |
+
st.audio(audio_bytes, format="audio/wav")
|
430 |
+
return filename
|
431 |
+
return None
|
432 |
+
|
433 |
+
|
434 |
+
|
435 |
+
def truncate_document(document, length):
|
436 |
+
return document[:length]
|
437 |
+
|
438 |
+
def divide_document(document, max_length):
|
439 |
+
return [document[i:i+max_length] for i in range(0, len(document), max_length)]
|
440 |
+
|
441 |
+
def get_table_download_link(file_path):
|
442 |
+
with open(file_path, 'r') as file:
|
443 |
+
try:
|
444 |
+
data = file.read()
|
445 |
+
except:
|
446 |
+
st.write('')
|
447 |
+
return file_path
|
448 |
+
b64 = base64.b64encode(data.encode()).decode()
|
449 |
+
file_name = os.path.basename(file_path)
|
450 |
+
ext = os.path.splitext(file_name)[1] # get the file extension
|
451 |
+
if ext == '.txt':
|
452 |
+
mime_type = 'text/plain'
|
453 |
+
elif ext == '.py':
|
454 |
+
mime_type = 'text/plain'
|
455 |
+
elif ext == '.xlsx':
|
456 |
+
mime_type = 'text/plain'
|
457 |
+
elif ext == '.csv':
|
458 |
+
mime_type = 'text/plain'
|
459 |
+
elif ext == '.htm':
|
460 |
+
mime_type = 'text/html'
|
461 |
+
elif ext == '.md':
|
462 |
+
mime_type = 'text/markdown'
|
463 |
+
else:
|
464 |
+
mime_type = 'application/octet-stream' # general binary data type
|
465 |
+
href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
|
466 |
+
return href
|
467 |
+
|
468 |
+
def CompressXML(xml_text):
|
469 |
+
root = ET.fromstring(xml_text)
|
470 |
+
for elem in list(root.iter()):
|
471 |
+
if isinstance(elem.tag, str) and 'Comment' in elem.tag:
|
472 |
+
elem.parent.remove(elem)
|
473 |
+
return ET.tostring(root, encoding='unicode', method="xml")
|
474 |
+
|
475 |
+
def read_file_content(file,max_length):
|
476 |
+
if file.type == "application/json":
|
477 |
+
content = json.load(file)
|
478 |
+
return str(content)
|
479 |
+
elif file.type == "text/html" or file.type == "text/htm":
|
480 |
+
content = BeautifulSoup(file, "html.parser")
|
481 |
+
return content.text
|
482 |
+
elif file.type == "application/xml" or file.type == "text/xml":
|
483 |
+
tree = ET.parse(file)
|
484 |
+
root = tree.getroot()
|
485 |
+
xml = CompressXML(ET.tostring(root, encoding='unicode'))
|
486 |
+
return xml
|
487 |
+
elif file.type == "text/markdown" or file.type == "text/md":
|
488 |
+
md = mistune.create_markdown()
|
489 |
+
content = md(file.read().decode())
|
490 |
+
return content
|
491 |
+
elif file.type == "text/plain":
|
492 |
+
return file.getvalue().decode()
|
493 |
+
else:
|
494 |
+
return ""
|
495 |
+
|
496 |
+
def extract_mime_type(file):
|
497 |
+
# Check if the input is a string
|
498 |
+
if isinstance(file, str):
|
499 |
+
pattern = r"type='(.*?)'"
|
500 |
+
match = re.search(pattern, file)
|
501 |
+
if match:
|
502 |
+
return match.group(1)
|
503 |
+
else:
|
504 |
+
raise ValueError(f"Unable to extract MIME type from {file}")
|
505 |
+
# If it's not a string, assume it's a streamlit.UploadedFile object
|
506 |
+
elif isinstance(file, streamlit.UploadedFile):
|
507 |
+
return file.type
|
508 |
+
else:
|
509 |
+
raise TypeError("Input should be a string or a streamlit.UploadedFile object")
|
510 |
+
|
511 |
+
|
512 |
+
|
513 |
+
def extract_file_extension(file):
|
514 |
+
# get the file name directly from the UploadedFile object
|
515 |
+
file_name = file.name
|
516 |
+
pattern = r".*?\.(.*?)$"
|
517 |
+
match = re.search(pattern, file_name)
|
518 |
+
if match:
|
519 |
+
return match.group(1)
|
520 |
+
else:
|
521 |
+
raise ValueError(f"Unable to extract file extension from {file_name}")
|
522 |
+
|
523 |
+
def pdf2txt(docs):
|
524 |
+
text = ""
|
525 |
+
for file in docs:
|
526 |
+
file_extension = extract_file_extension(file)
|
527 |
+
# print the file extension
|
528 |
+
st.write(f"File type extension: {file_extension}")
|
529 |
+
|
530 |
+
# read the file according to its extension
|
531 |
+
try:
|
532 |
+
if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:
|
533 |
+
text += file.getvalue().decode('utf-8')
|
534 |
+
elif file_extension.lower() == 'pdf':
|
535 |
+
from PyPDF2 import PdfReader
|
536 |
+
pdf = PdfReader(BytesIO(file.getvalue()))
|
537 |
+
for page in range(len(pdf.pages)):
|
538 |
+
text += pdf.pages[page].extract_text() # new PyPDF2 syntax
|
539 |
+
except Exception as e:
|
540 |
+
st.write(f"Error processing file {file.name}: {e}")
|
541 |
+
return text
|
542 |
+
|
543 |
+
def txt2chunks(text):
|
544 |
+
text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
|
545 |
+
return text_splitter.split_text(text)
|
546 |
+
|
547 |
+
def vector_store(text_chunks):
|
548 |
+
key = os.getenv('OPENAI_API_KEY')
|
549 |
+
embeddings = OpenAIEmbeddings(openai_api_key=key)
|
550 |
+
return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
|
551 |
+
|
552 |
+
def get_chain(vectorstore):
|
553 |
+
llm = ChatOpenAI()
|
554 |
+
memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
|
555 |
+
return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
|
556 |
+
|
557 |
+
def divide_prompt(prompt, max_length):
|
558 |
+
words = prompt.split()
|
559 |
+
chunks = []
|
560 |
+
current_chunk = []
|
561 |
+
current_length = 0
|
562 |
+
for word in words:
|
563 |
+
if len(word) + current_length <= max_length:
|
564 |
+
current_length += len(word) + 1 # Adding 1 to account for spaces
|
565 |
+
current_chunk.append(word)
|
566 |
+
else:
|
567 |
+
chunks.append(' '.join(current_chunk))
|
568 |
+
current_chunk = [word]
|
569 |
+
current_length = len(word)
|
570 |
+
chunks.append(' '.join(current_chunk)) # Append the final chunk
|
571 |
+
return chunks
|
572 |
+
|
573 |
+
def create_zip_of_files(files):
|
574 |
+
"""
|
575 |
+
Create a zip file from a list of files.
|
576 |
+
"""
|
577 |
+
zip_name = "all_files.zip"
|
578 |
+
with zipfile.ZipFile(zip_name, 'w') as zipf:
|
579 |
+
for file in files:
|
580 |
+
zipf.write(file)
|
581 |
+
return zip_name
|
582 |
+
|
583 |
+
|
584 |
+
def get_zip_download_link(zip_file):
|
585 |
+
"""
|
586 |
+
Generate a link to download the zip file.
|
587 |
+
"""
|
588 |
+
with open(zip_file, 'rb') as f:
|
589 |
+
data = f.read()
|
590 |
+
b64 = base64.b64encode(data).decode()
|
591 |
+
href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
|
592 |
+
return href
|
593 |
+
|
594 |
+
|
595 |
+
def main():
|
596 |
+
|
597 |
+
# Audio, transcribe, GPT:
|
598 |
+
filename = save_and_play_audio(audio_recorder)
|
599 |
+
|
600 |
+
if filename is not None:
|
601 |
+
try:
|
602 |
+
transcription = transcribe_audio(filename, "whisper-1")
|
603 |
+
except:
|
604 |
+
st.write(' ')
|
605 |
+
st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
|
606 |
+
filename = None
|
607 |
+
|
608 |
+
# prompt interfaces
|
609 |
+
user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)
|
610 |
+
|
611 |
+
# file section interface for prompts against large documents as context
|
612 |
+
collength, colupload = st.columns([2,3]) # adjust the ratio as needed
|
613 |
+
with collength:
|
614 |
+
max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)
|
615 |
+
with colupload:
|
616 |
+
uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])
|
617 |
+
|
618 |
+
|
619 |
+
# Document section chat
|
620 |
+
|
621 |
+
document_sections = deque()
|
622 |
+
document_responses = {}
|
623 |
+
if uploaded_file is not None:
|
624 |
+
file_content = read_file_content(uploaded_file, max_length)
|
625 |
+
document_sections.extend(divide_document(file_content, max_length))
|
626 |
+
if len(document_sections) > 0:
|
627 |
+
if st.button("ποΈ View Upload"):
|
628 |
+
st.markdown("**Sections of the uploaded file:**")
|
629 |
+
for i, section in enumerate(list(document_sections)):
|
630 |
+
st.markdown(f"**Section {i+1}**\n{section}")
|
631 |
+
st.markdown("**Chat with the model:**")
|
632 |
+
for i, section in enumerate(list(document_sections)):
|
633 |
+
if i in document_responses:
|
634 |
+
st.markdown(f"**Section {i+1}**\n{document_responses[i]}")
|
635 |
+
else:
|
636 |
+
if st.button(f"Chat about Section {i+1}"):
|
637 |
+
st.write('Reasoning with your inputs...')
|
638 |
+
response = chat_with_model(user_prompt, section, model_choice)
|
639 |
+
document_responses[i] = response
|
640 |
+
filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)
|
641 |
+
create_file(filename, user_prompt, response, should_save)
|
642 |
+
st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
|
643 |
+
|
644 |
+
if st.button('π¬ Chat'):
|
645 |
+
st.write('Reasoning with your inputs...')
|
646 |
+
|
647 |
+
# Divide the user_prompt into smaller sections
|
648 |
+
user_prompt_sections = divide_prompt(user_prompt, max_length)
|
649 |
+
full_response = ''
|
650 |
+
for prompt_section in user_prompt_sections:
|
651 |
+
# Process each section with the model
|
652 |
+
response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)
|
653 |
+
full_response += response + '\n' # Combine the responses
|
654 |
+
response = full_response
|
655 |
+
filename = generate_filename(user_prompt, choice)
|
656 |
+
create_file(filename, user_prompt, response, should_save)
|
657 |
+
st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)
|
658 |
+
|
659 |
+
all_files = glob.glob("*.*")
|
660 |
+
all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20] # exclude files with short names
|
661 |
+
all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order
|
662 |
+
|
663 |
+
|
664 |
+
# Sidebar buttons Download All and Delete All
|
665 |
+
colDownloadAll, colDeleteAll = st.sidebar.columns([3,3])
|
666 |
+
with colDownloadAll:
|
667 |
+
if st.button("β¬οΈ Download All"):
|
668 |
+
zip_file = create_zip_of_files(all_files)
|
669 |
+
st.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)
|
670 |
+
with colDeleteAll:
|
671 |
+
if st.button("π Delete All"):
|
672 |
+
for file in all_files:
|
673 |
+
os.remove(file)
|
674 |
+
st.experimental_rerun()
|
675 |
+
|
676 |
+
# Sidebar of Files Saving History and surfacing files as context of prompts and responses
|
677 |
+
file_contents=''
|
678 |
+
next_action=''
|
679 |
+
for file in all_files:
|
680 |
+
col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) # adjust the ratio as needed
|
681 |
+
with col1:
|
682 |
+
if st.button("π", key="md_"+file): # md emoji button
|
683 |
+
with open(file, 'r') as f:
|
684 |
+
file_contents = f.read()
|
685 |
+
next_action='md'
|
686 |
+
with col2:
|
687 |
+
st.markdown(get_table_download_link(file), unsafe_allow_html=True)
|
688 |
+
with col3:
|
689 |
+
if st.button("π", key="open_"+file): # open emoji button
|
690 |
+
with open(file, 'r') as f:
|
691 |
+
file_contents = f.read()
|
692 |
+
next_action='open'
|
693 |
+
with col4:
|
694 |
+
if st.button("π", key="read_"+file): # search emoji button
|
695 |
+
with open(file, 'r') as f:
|
696 |
+
file_contents = f.read()
|
697 |
+
next_action='search'
|
698 |
+
with col5:
|
699 |
+
if st.button("π", key="delete_"+file):
|
700 |
+
os.remove(file)
|
701 |
+
st.experimental_rerun()
|
702 |
+
|
703 |
+
if len(file_contents) > 0:
|
704 |
+
if next_action=='open':
|
705 |
+
file_content_area = st.text_area("File Contents:", file_contents, height=500)
|
706 |
+
if next_action=='md':
|
707 |
+
st.markdown(file_contents)
|
708 |
+
if next_action=='search':
|
709 |
+
file_content_area = st.text_area("File Contents:", file_contents, height=500)
|
710 |
+
st.write('Reasoning with your inputs...')
|
711 |
+
response = chat_with_model(user_prompt, file_contents, model_choice)
|
712 |
+
filename = generate_filename(file_contents, choice)
|
713 |
+
create_file(filename, user_prompt, response, should_save)
|
714 |
+
|
715 |
+
st.experimental_rerun()
|
716 |
+
|
717 |
+
if __name__ == "__main__":
|
718 |
+
main()
|
719 |
+
|
720 |
+
load_dotenv()
|
721 |
+
st.write(css, unsafe_allow_html=True)
|
722 |
+
|
723 |
+
st.header("Chat with documents :books:")
|
724 |
+
user_question = st.text_input("Ask a question about your documents:")
|
725 |
+
if user_question:
|
726 |
+
process_user_input(user_question)
|
727 |
+
|
728 |
+
with st.sidebar:
|
729 |
+
st.subheader("Your documents")
|
730 |
+
docs = st.file_uploader("import documents", accept_multiple_files=True)
|
731 |
+
with st.spinner("Processing"):
|
732 |
+
raw = pdf2txt(docs)
|
733 |
+
if len(raw) > 0:
|
734 |
+
length = str(len(raw))
|
735 |
+
text_chunks = txt2chunks(raw)
|
736 |
+
vectorstore = vector_store(text_chunks)
|
737 |
+
st.session_state.conversation = get_chain(vectorstore)
|
738 |
+
st.markdown('# AI Search Index of Length:' + length + ' Created.') # add timing
|
739 |
+
filename = generate_filename(raw, 'txt')
|
740 |
+
create_file(filename, raw, '', should_save)
|