Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-import asyncio
 import logging
 from datetime import datetime, timedelta
 from newsapi.newsapi_client import NewsApiClient
@@ -8,15 +7,40 @@ import yfinance as yf
 import pandas as pd
 import ta
 import gradio as gr
+from groq import Groq
 
 # Set up logging
 logging.basicConfig(level=logging.WARNING, format='%(asctime)s - %(levelname)s - %(message)s')
 
-# Retrieve API key from environment variables
+# Retrieve API keys from environment variables
 NEWSAPI_KEY = os.getenv("NEWSAPI_KEY")
-
-# Fetch financial news
-def fetch_financial_news(stock_symbol=None, page_size=5, days=2):
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+
+# Initialize Groq client
+groq_client = Groq(api_key=GROQ_API_KEY)
+
+# Use Groq's Llama 3 model for decision making
+MODEL = "llama3-70b-8192"
+
+# Define the list of companies and their stock symbols
+top_companies = [
+    {"name": "Tesla", "symbol": "TSLA"},
+    {"name": "Meta (Facebook)", "symbol": "META"},
+    {"name": "Visa", "symbol": "V"},
+    {"name": "Procter & Gamble", "symbol": "PG"},
+    {"name": "Coca-Cola", "symbol": "KO"},
+    {"name": "NVIDIA", "symbol": "NVDA"},
+    {"name": "Johnson & Johnson", "symbol": "JNJ"},
+    {"name": "Exxon Mobil", "symbol": "XOM"},
+    {"name": "Apple", "symbol": "AAPL"},
+    {"name": "Microsoft", "symbol": "MSFT"},
+    {"name": "Amazon", "symbol": "AMZN"},
+    {"name": "Google (Alphabet)", "symbol": "GOOGL"},
+    
+]
+
+# Fetch financial news with sentiment analysis
+def fetch_financial_news_with_sentiment(stock_symbol=None, page_size=5, days=1):
     try:
         newsapi = NewsApiClient(api_key=NEWSAPI_KEY)
         query = stock_symbol if stock_symbol else "financial news"
@@ -32,16 +56,21 @@ def fetch_financial_news(stock_symbol=None, page_size=5, days=2):
             page_size=page_size
         )
 
-        results = []
+        news_results = []
+        sentiment_results = []
+
         for article in articles.get('articles', []):
             title = article.get('title', '[Title Unavailable]')
             description = article.get('description', '[Description Unavailable]')
             url = article.get('url', 'URL Unavailable')
-            results.append(f"Title: {title}\nDescription: {description}\nURL: {url}")
+            sentiment = analyze_sentiment(title) if title else "Neutral"
 
-        return "\n\n".join(results)
+            news_results.append(f"Title: {title}\nDescription: {description}\nURL: {url}")
+            sentiment_results.append(f"Sentiment: {sentiment}")
+
+        return "\n\n".join(news_results), "\n\n".join(sentiment_results)
     except Exception as e:
-        return f"Error fetching news: {e}"
+        return f"Error fetching news: {e}", ""
 
 # Perform sentiment analysis
 def analyze_sentiment(text):
@@ -85,20 +114,53 @@ def fetch_technical_data(stock_symbol):
     except Exception as e:
         return f"Error fetching technical data: {e}"
 
+# Generate buy/hold/sell recommendation using Groq
+def generate_recommendation(news, technical_data):
+    prompt = f"Based on the following news:\n{news}\nAnd the technical indicators:\n{technical_data}\nWhat would you recommend: Buy, Hold, or Sell? Provide a brief explanation."
+    
+    response = groq_client.chat.completions.create(
+        model=MODEL,
+        messages=[
+            {"role": "system", "content": "You are a financial analyst providing stock recommendations."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=150
+    )
+    
+    return response.choices[0].message.content.strip()
+
 # Define Gradio interface
 def analyze_stock(stock_symbol):
-    news = fetch_financial_news(stock_symbol)
-    technical_data = fetch_technical_data(stock_symbol)
-    return news, technical_data
+    symbol = stock_symbol.split('(')[-1].split(')')[0]
+    news, sentiment = fetch_financial_news_with_sentiment(symbol, days=1)
+    technical_data = fetch_technical_data(symbol)
+    recommendation = generate_recommendation(news, technical_data)
+    return news, sentiment, technical_data, recommendation
 
 with gr.Blocks() as demo:
     gr.Markdown("## Financial News and Technical Analysis Tool")
-    stock_input = gr.Textbox(label="Enter Stock Symbol (e.g., AAPL, TSLA)")
-    news_output = gr.Textbox(label="Financial News", interactive=False)
-    tech_output = gr.Textbox(label="Technical Analysis", interactive=False)
-    analyze_button = gr.Button("Analyze")
 
-    analyze_button.click(analyze_stock, inputs=[stock_input], outputs=[news_output, tech_output])
+    with gr.Row():
+        stock_input = gr.Dropdown(
+            choices=[f"{company['name']} ({company['symbol']})" for company in top_companies],
+            label="Enter Stock Symbol (currently supports only a few companies)",
+            info="Select a company from the dropdown"
+        )
+        analyze_button = gr.Button("Analyze")
+
+    recommendation_output = gr.Textbox(label="Recommendation", interactive=False)
+    
+    with gr.Row():
+        news_output = gr.Textbox(label="Financial News", interactive=False, lines=10)
+        sentiment_output = gr.Textbox(label="Sentiment Analysis", interactive=False, lines=10)
+    technical_output = gr.Textbox(label="Technical Analysis", interactive=False)
+
+    analyze_button.click(
+        analyze_stock,
+        inputs=[stock_input],
+        outputs=[news_output, sentiment_output, technical_output, recommendation_output]
+    )
 
 if __name__ == "__main__":
     demo.launch()
+