Create app.py

app.py

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+# app.py
+"""
+Yahoo Finance Sentiment Analysis with Gemma LLM
+Hugging Face Space Application
+"""
+
+import gradio as gr
+import pandas as pd
+from datetime import datetime
+from utils import YahooFinanceScraper, SentimentAnalyzer, LLMAnalyzer
+from config import POPULAR_STOCKS
+import plotly.graph_objects as go
+
+# Initialize components
+print("Initializing application...")
+scraper = YahooFinanceScraper()
+sentiment_analyzer = SentimentAnalyzer()
+llm_analyzer = LLMAnalyzer()
+print("Application ready!")
+
+
+def analyze_stock_news(symbol: str, num_articles: int = 10):
+    """
+    Main function to analyze stock news
+    
+    Args:
+        symbol: Stock ticker symbol
+        num_articles: Number of articles to analyze
+        
+    Returns:
+        Tuple of (summary, dataframe, chart, llm_insights)
+    """
+    try:
+        # Fetch news
+        articles = scraper.get_stock_news(symbol, num_articles)
+        
+        if not articles:
+            return "No news found for this symbol.", None, None, "No articles to analyze."
+        
+        # Analyze sentiments
+        sentiments = []
+        for article in articles:
+            text = f"{article['title']}. {article.get('summary', '')}"
+            sentiment = sentiment_analyzer.analyze_comprehensive(text)
+            sentiments.append(sentiment)
+        
+        # Generate LLM insights
+        market_summary = llm_analyzer.summarize_news(articles)
+        investment_insight = llm_analyzer.generate_investment_insight(symbol, articles, sentiments)
+        
+        # Create dataframe
+        df_data = []
+        for article, sentiment in zip(articles, sentiments):
+            df_data.append({
+                'Title': article['title'],
+                'Publisher': article['publisher'],
+                'Sentiment': sentiment['sentiment_label'],
+                'Score': f"{sentiment['combined_score']:.3f}",
+                'Confidence': f"{sentiment['confidence']:.2%}",
+                'VADER': f"{sentiment['vader']['compound']:.3f}",
+                'FinBERT +': f"{sentiment['finbert']['positive']:.3f}",
+                'FinBERT -': f"{sentiment['finbert']['negative']:.3f}",
+            })
+        
+        df = pd.DataFrame(df_data)
+        
+        # Create visualization
+        sentiment_counts = df['Sentiment'].value_counts()
+        fig = go.Figure(data=[
+            go.Bar(
+                x=sentiment_counts.index,
+                y=sentiment_counts.values,
+                marker_color=['#00cc66' if x=='Positive' else '#ff6666' if x=='Negative' else '#999999' 
+                              for x in sentiment_counts.index]
+            )
+        ])
+        fig.update_layout(
+            title=f"Sentiment Distribution for {symbol}",
+            xaxis_title="Sentiment",
+            yaxis_title="Number of Articles",
+            height=400
+        )
+        
+        # Calculate statistics
+        avg_score = sum(s['combined_score'] for s in sentiments) / len(sentiments)
+        positive_pct = (sentiment_counts.get('Positive', 0) / len(sentiments)) * 100
+        negative_pct = (sentiment_counts.get('Negative', 0) / len(sentiments)) * 100
+        
+        summary = f"""
+## 📊 Analysis Summary for {symbol}
+
+**Total Articles Analyzed:** {len(articles)}
+
+**Sentiment Distribution:**
+- 🟢 Positive: {sentiment_counts.get('Positive', 0)} ({positive_pct:.1f}%)
+- 🔴 Negative: {sentiment_counts.get('Negative', 0)} ({negative_pct:.1f}%)
+- ⚪ Neutral: {sentiment_counts.get('Neutral', 0)} ({100-positive_pct-negative_pct:.1f}%)
+
+**Average Sentiment Score:** {avg_score:.3f}
+
+**Overall Sentiment:** {"🟢 Positive" if avg_score > 0.05 else "🔴 Negative" if avg_score < -0.05 else "⚪ Neutral"}
+        """
+        
+        llm_insights = f"""
+## 🤖 AI-Generated Insights (Powered by Gemma)
+
+### Market Summary:
+{market_summary}
+
+### Investment Perspective:
+{investment_insight}
+
+---
+*Note: These insights are generated by AI and should not be considered as financial advice.*
+        """
+        
+        return summary, df, fig, llm_insights
+        
+    except Exception as e:
+        return f"Error: {str(e)}", None, None, "Error generating insights."
+
+
+def analyze_single_headline(headline: str):
+    """
+    Analyze a single headline
+    
+    Args:
+        headline: News headline text
+        
+    Returns:
+        Analysis results
+    """
+    try:
+        sentiment = sentiment_analyzer.analyze_comprehensive(headline)
+        
+        # Create a dummy article dict for LLM analysis
+        article = {'title': headline, 'summary': ''}
+        explanation = llm_analyzer.analyze_sentiment_context(article, sentiment)
+        
+        result = f"""
+## Sentiment Analysis Results
+
+**Headline:** {headline}
+
+**Overall Sentiment:** {sentiment['sentiment_label']} (Score: {sentiment['combined_score']:.3f})
+**Confidence:** {sentiment['confidence']:.2%}
+
+### Detailed Scores:
+- **VADER Compound:** {sentiment['vader']['compound']:.3f}
+- **FinBERT Positive:** {sentiment['finbert']['positive']:.3%}
+- **FinBERT Negative:** {sentiment['finbert']['negative']:.3%}
+- **FinBERT Neutral:** {sentiment['finbert']['neutral']:.3%}
+
+### AI Explanation:
+{explanation}
+        """
+        
+        return result
+        
+    except Exception as e:
+        return f"Error analyzing headline: {str(e)}"
+
+
+# Create Gradio Interface
+with gr.Blocks(title="Yahoo Finance Sentiment Analyzer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 📈 Yahoo Finance Sentiment Analyzer
+    ### Powered by FinBERT + Gemma LLM
+    
+    Analyze market sentiment from Yahoo Finance news using advanced NLP and AI.
+    """)
+    
+    with gr.Tabs():
+        # Tab 1: Stock Analysis
+        with gr.Tab("📊 Stock Sentiment Analysis"):
+            gr.Markdown("### Analyze sentiment of news for any stock symbol")
+            
+            with gr.Row():
+                with gr.Column(scale=2):
+                    stock_input = gr.Textbox(
+                        label="Stock Symbol",
+                        placeholder="e.g., AAPL, GOOGL, TSLA",
+                        value="AAPL"
+                    )
+                with gr.Column(scale=1):
+                    num_articles = gr.Slider(
+                        minimum=5,
+                        maximum=20,
+                        value=10,
+                        step=1,
+                        label="Number of Articles"
+                    )
+            
+            gr.Markdown("**Quick Select:**")
+            quick_buttons = []
+            with gr.Row():
+                for stock in POPULAR_STOCKS[:5]:
+                    btn = gr.Button(stock, size="sm")
+                    quick_buttons.append(btn)
+            with gr.Row():
+                for stock in POPULAR_STOCKS[5:10]:
+                    btn = gr.Button(stock, size="sm")
+                    quick_buttons.append(btn)
+            
+            analyze_btn = gr.Button("🔍 Analyze News", variant="primary", size="lg")
+            
+            summary_output = gr.Markdown(label="Summary")
+            insights_output = gr.Markdown(label="AI Insights")
+            chart_output = gr.Plot(label="Sentiment Distribution")
+            table_output = gr.Dataframe(label="Detailed Results")
+            
+            # Button actions
+            analyze_btn.click(
+                fn=analyze_stock_news,
+                inputs=[stock_input, num_articles],
+                outputs=[summary_output, table_output, chart_output, insights_output]
+            )
+            
+            # Quick select buttons
+            for btn in quick_buttons:
+                btn.click(
+                    fn=lambda x: x,
+                    inputs=[btn],
+                    outputs=[stock_input]
+                )
+        
+        # Tab 2: Single Headline Analysis
+        with gr.Tab("📰 Single Headline Analyzer"):
+            gr.Markdown("### Analyze sentiment of a single news headline")
+            
+            headline_input = gr.Textbox(
+                label="News Headline",
+                placeholder="Enter a financial news headline...",
+                lines=3
+            )
+            
+            gr.Markdown("**Example Headlines:**")
+            example_headlines = [
+                "Apple reaches all-time high as iPhone sales surge",
+                "Tesla stock plummets amid production concerns",
+                "Fed maintains interest rates, markets remain stable"
+            ]
+            
+            with gr.Row():
+                for example in example_headlines:
+                    gr.Button(example[:50] + "...", size="sm").click(
+                        fn=lambda x: x,
+                        inputs=[gr.Textbox(value=example, visible=False)],
+                        outputs=[headline_input]
+                    )
+            
+            analyze_headline_btn = gr.Button("🔍 Analyze Headline", variant="primary")
+            headline_output = gr.Markdown(label="Analysis Results")
+            
+            analyze_headline_btn.click(
+                fn=analyze_single_headline,
+                inputs=[headline_input],
+                outputs=[headline_output]
+            )
+        
+        # Tab 3: About
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown("""
+            ## About This Application
+            
+            This application analyzes sentiment from Yahoo Finance news using multiple advanced techniques:
+            
+            ### 🛠️ Technologies Used:
+            
+            1. **VADER Sentiment Analysis**
+               - Rule-based sentiment analysis
+               - Good for general text sentiment
+            
+            2. **FinBERT**
+               - BERT model fine-tuned for financial text
+               - Specialized in financial sentiment analysis
+               - Model: `ProsusAI/finbert`
+            
+            3. **Gemma LLM**
+               - Google's Gemma language model
+               - Generates human-like insights and summaries
+               - Model: `google/gemma-2-2b-it`
+            
+            ### 📊 Features:
+            
+            - Real-time news scraping from Yahoo Finance
+            - Multi-model sentiment analysis
+            - AI-generated market insights
+            - Interactive visualizations
+            - Batch and single headline analysis
+            
+            ### 📝 Sentiment Scores:
+            
+            - **Positive**: Score > 0.05
+            - **Negative**: Score < -0.05
+            - **Neutral**: -0.05 ≤ Score ≤ 0.05
+            
+            ### ⚠️ Disclaimer:
+            
+            This tool is for educational and research purposes only. 
+            The sentiment analysis and AI-generated insights should NOT be used as financial advice. 
+            Always do your own research and consult with financial professionals before making investment decisions.
+            
+            ---
+            
+            **Created with ❤️ using Hugging Face Spaces**
+            """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()
+
+