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# Matlplotlib chart
# New thread
# Multimodal input
# File search
# Function: Tavily API
# https://platform.openai.com/playground/assistants
# https://platform.openai.com/docs/api-reference/assistants/createAssistant
# https://cookbook.openai.com/examples/assistants_api_overview_python
import gradio as gr
import datetime, openai, os, time
from openai import OpenAI
from utils import show_json
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
assistant, thread = None, None
def create_assistant(client):
assistant = client.beta.assistants.create(
name="Python Code Generator",
instructions=(
"You are a Python programming language expert that "
"generates Pylint-compliant code and explains it. "
"Only execute code when explicitly asked to."
),
model="gpt-4o",
tools=[
{"type": "code_interpreter"},
],
)
show_json("assistant", assistant)
return assistant
def load_assistant(client):
assistant = client.beta.assistants.retrieve("asst_rA2o181bBD633oWoaiImdzwG")
show_json("assistant", assistant)
return assistant
def create_thread(client):
thread = client.beta.threads.create()
show_json("thread", thread)
return thread
def create_message(client, thread, msg):
message = client.beta.threads.messages.create(
role="user",
thread_id=thread.id,
content=msg,
)
show_json("message", message)
return message
def create_run(client, assistant, thread):
run = client.beta.threads.runs.create(
assistant_id=assistant.id,
thread_id=thread.id,
)
show_json("run", run)
return run
def wait_on_run(client, thread, run):
while run.status == "queued" or run.status == "in_progress":
run = client.beta.threads.runs.retrieve(
thread_id=thread.id,
run_id=run.id,
)
time.sleep(0.25)
show_json("run", run)
return run
def get_run_steps(client, thread, run):
run_steps = client.beta.threads.runs.steps.list(
thread_id=thread.id,
run_id=run.id,
order="asc",
)
show_json("run_steps", run_steps)
return run_steps
def get_run_step_details(run_steps):
for step in run_steps.data:
step_details = step.step_details
show_json("step_details", step_details)
def get_messages(client, thread):
messages = client.beta.threads.messages.list(
thread_id=thread.id
)
show_json("messages", messages)
return messages
def extract_content_values(data):
content_values = []
for item in data.data:
for content in item.content:
if content.type == "text":
content_values.append(content.text.value)
return content_values
def chat(message, history):
if not message:
raise gr.Error("Message is required.")
global client, assistant, thread
if assistant == None:
assistant = load_assistant(client)
if thread == None or len(history) == 0:
thread = create_thread(client)
create_message(client, thread, message)
run = create_run(client, assistant, thread)
run = wait_on_run(client, thread, run)
run_steps = get_run_steps(client, thread, run)
get_run_step_details(run_steps)
messages = get_messages(client, thread)
content_values = extract_content_values(messages)
return content_values[0]
gr.ChatInterface(
fn=chat,
chatbot=gr.Chatbot(height=350),
textbox=gr.Textbox(placeholder="Ask anything", container=False, scale=7),
title="Python Code Generator",
description="The assistant can generate, explain, fix, optimize, document, test, and generally help with code. It can also execute code.",
examples=[
["Generate: NumPy/Pandas/Matplotlib & yfinance trading app"],
["Explain: r'^(?=.*[A-Z])(?=.*[a-z])(?=.*[0-9])(?=.*[\\W]).{8,}$'"],
["Fix: x = [5, 2, 1, 3, 4]; print(x.sort())"],
["Optimize: x = []; for i in range(0, 10000): x.append(i)"],
["Generate and run: Print the first 20 fibbonaci numbers"],
[f"Execute: Today is {datetime.date.today()}. Create a plot showing stock gain YTD for NVDA, MSFT, AAPL, and GOOG, x-axis is 'Day' and y-axis is 'YTD Gain %'. Return the result, don't persist to storage."],
],
cache_examples=False,
).launch() |