app.py

import pandas as pd
import openai
import gradio as gr
import requests
import yfinance as yf
import json  # Import json module
import os

openai.api_key = os.getenv('yek_oaipen')

def stock_price(ticker):
  stock = yf.Ticker(ticker)
  price = stock.history(period='1d')
  latest_price = price['Close'].iloc[0]
  cashflow = stock.cashflow

  return latest_price,cashflow

functions =[
     {
        "name": "stock_price",
        "description": "Get the current stock price",
        # "strict": True,
        "parameters": {
          "type": "object",
          "properties": {
            "symbol": {
              "type": "string",
              "description": "The stock symbol"
            }
          },
          "additionalProperties": False,
          "required": [
            "symbol"
          ]
        }
      }
]



#function will be used in the below code. structure is copied from the documentation.
def stock_chat(user_message):
    messages=[]
    messages.append({"role": "user", "content": user_message})
    messages.append({"role": "assistant", "content": "You are a stock information bot. Answer only related to stock information."})



        # Sending Initial Message to OpenAI
    response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            temperature = 0,
            max_tokens=50,
            top_p=0.5,
            frequency_penalty=0,
            presence_penalty=0,
            messages=messages,
            functions=functions
        )



    #Handling Function Calls and Fetching stocks Data
    try:
        function_call = response['choices'][0]['message']['function_call']
        arguments = eval(function_call['arguments'])
        # arguments = json.loads(function_call['arguments'])

        # Fetch stocks data using the extracted arguments
        stock_data = stock_price(arguments['symbol'])

        # Append the function call and stocks data to the messages
        messages.append({"role": "assistant", "content": None, "function_call": {"name": "stock_price", "arguments": str(arguments)}})
        messages.append({"role": "function", "name": "stock_price", "content": str(stock_data)})

        #magic of llm
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=messages
        )

        return response['choices'][0]['message']['content']
    except Exception as e:
        return "I'm here to provide stocks information. Please ask me questions related to Stock Market."


# Gradio Interface
iface = gr.Interface(
    fn=stock_chat,
    inputs=gr.Textbox(lines=2, placeholder="Ask me about stock info like - prices, financial, cashflow...", label="Stock Queries"),
    outputs=gr.Textbox(label="Your stock query result using Openai advanced model"),
    title="Stock Information Assistant By Parvez Alam",
    description="Ask me about stock prices and financial data.",
    theme="default"
)

iface.launch()