Create app.py

app.py

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+# Install required packages
+#!pip install gradio yfinance transformers torch pandas plotly requests beautifulsoup4
+
+import gradio as gr
+import yfinance as yf
+import pandas as pd
+import torch
+import plotly.graph_objects as go
+import plotly.express as px
+from transformers import pipeline
+from datetime import datetime, timedelta
+import requests
+from bs4 import BeautifulSoup
+import numpy as np
+
+# Check for GPU availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Initialize models
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device)
+sentiment_analyzer = pipeline("sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis", device=device)
+
+class CompanyResearchAgent:
+    def __init__(self):
+        self.cache = {}
+    
+    def get_stock_data(self, symbol, period="1y"):
+        """Fetch stock data using yfinance"""
+        try:
+            stock = yf.Ticker(symbol)
+            hist = stock.history(period=period)
+            return stock, hist
+        except Exception as e:
+            return None, None
+    
+    def create_stock_chart(self, hist):
+        """Create interactive stock price chart"""
+        if hist is None or hist.empty:
+            return None
+            
+        fig = go.Figure()
+        fig.add_trace(go.Candlestick(
+            x=hist.index,
+            open=hist['Open'],
+            high=hist['High'],
+            low=hist['Low'],
+            close=hist['Close'],
+            name='Stock Price'
+        ))
+        fig.update_layout(
+            title="Stock Price History",
+            yaxis_title="Price",
+            xaxis_title="Date",
+            template="plotly_dark"
+        )
+        return fig
+    
+    def get_news_sentiment(self, company_name):
+        """Analyze news sentiment"""
+        try:
+            url = f"https://news.google.com/rss/search?q={company_name}+when:7d"
+            response = requests.get(url)
+            soup = BeautifulSoup(response.content, 'xml')
+            titles = [item.title.text for item in soup.find_all('item')[:5]]
+            
+            sentiments = sentiment_analyzer(titles)
+            sentiment_scores = [s['score'] for s in sentiments]
+            avg_sentiment = sum(sentiment_scores) / len(sentiment_scores)
+            
+            return {
+                'average_sentiment': round(avg_sentiment, 2),
+                'recent_news': titles
+            }
+        except Exception as e:
+            return {
+                'average_sentiment': 0,
+                'recent_news': ['Unable to fetch news']
+            }
+    
+    def generate_swot(self, stock, company_name):
+        """Generate SWOT analysis using company data"""
+        if stock is None:
+            return "Unable to generate SWOT analysis - invalid stock data"
+            
+        info = stock.info
+        
+        # Create SWOT analysis text
+        swot_text = f"""
+        Company Analysis for {company_name}:
+        Sector: {info.get('sector', 'N/A')}
+        Industry: {info.get('industry', 'N/A')}
+        Market Cap: ${info.get('marketCap', 0):,.2f}
+        P/E Ratio: {info.get('trailingPE', 'N/A')}
+        Revenue Growth: {info.get('revenueGrowth', 'N/A')}
+        """
+        
+        # Summarize SWOT analysis
+        summary = summarizer(swot_text, max_length=150, min_length=50)[0]['summary_text']
+        return summary
+    
+    def analyze_company(self, symbol):
+        """Main analysis function"""
+        try:
+            # Get stock data
+            stock, hist = self.get_stock_data(symbol)
+            if stock is None:
+                return "Invalid stock symbol", None, None, None
+            
+            # Create visualization
+            stock_chart = self.create_stock_chart(hist)
+            
+            # Get company info
+            info = stock.info
+            company_name = info.get('longName', symbol)
+            
+            # Generate SWOT analysis
+            swot_analysis = self.generate_swot(stock, company_name)
+            
+            # Get news sentiment
+            sentiment_data = self.get_news_sentiment(company_name)
+            
+            # Prepare company overview
+            # Fixed: Indentation adjusted to align with the function definition
+            company_overview = (
+                f"## {company_name} ({symbol})\n\n"
+                f"**Sector:** {info.get('sector', 'N/A')}\n"
+                f"**Industry:** {info.get('industry', 'N/A')}\n"
+                f"**Market Cap:** ${info.get('marketCap', 0):,.2f}\n"
+                f"**Current Price:** ${info.get('currentPrice', 0):,.2f}\n\n"
+                f"### News Sentiment Score: {sentiment_data['average_sentiment']}\n\n"
+                "Recent News:\n"
+                + "\n".join(f"- {news}" for news in sentiment_data['recent_news']) + "\n\n"
+                f"### SWOT Analysis Summary:\n"
+                f"{swot_analysis}"
+            )
+            
+            return company_overview, stock_chart, None, None
+        except Exception as e:
+            return f"Error analyzing company: {str(e)}", None, None, None
+
+# Create Gradio interface
+def create_interface():
+    agent = CompanyResearchAgent()
+    
+    with gr.Blocks(theme=gr.themes.Base()) as interface:
+        gr.Markdown("# Company Research Agent 📈")
+        gr.Markdown("Enter a stock symbol (e.g., AAPL, GOOGL, MSFT)")
+        
+        with gr.Row():
+            symbol_input = gr.Textbox(label="Stock Symbol")
+            analyze_btn = gr.Button("Analyze Company", variant="primary")
+        
+        with gr.Row():
+            with gr.Column():
+                overview_output = gr.Markdown(label="Company Overview")
+            with gr.Column():
+                chart_output = gr.Plot(label="Stock Price Chart")
+        
+        analyze_btn.click(
+            fn=agent.analyze_company,
+            inputs=[symbol_input],
+            outputs=[overview_output, chart_output]
+        )
+        
+    return interface
+
+# Launch the interface
+interface = create_interface()
+interface.launch(debug=True, share=True)