Update app.py

app.py

@@ -6,7 +6,7 @@ import json
 import os
 import plotly.express as px
 import altair as alt
-from utils import analyze_company_data  # Import the analysis function directly
+from utils import analyze_company_data, TextToSpeechConverter  # Import TextToSpeechConverter
 
 # Set page config
 st.set_page_config(
@@ -31,61 +31,163 @@ def process_company(company_name):
         # Call the analysis function directly from utils
         data = analyze_company_data(company_name)
         
-        # Generate audio if needed
+        # Generate Hindi audio if needed
         if 'summary' in data:
-            from gtts import gTTS
-            tts = gTTS(text=data['summary'], lang='en')
-            audio_path = os.path.join('audio_output', f'{company_name}_summary.mp3')
-            os.makedirs('audio_output', exist_ok=True)
-            tts.save(audio_path)
+            tts_converter = TextToSpeechConverter()
+            audio_path = tts_converter.generate_audio(data['summary'], f'{company_name}_summary')
             data['audio_path'] = audio_path
             
-        return data
+            return data
     except Exception as e:
         st.error(f"Error processing company: {str(e)}")
         return {"articles": [], "comparative_sentiment_score": {}, "final_sentiment_analysis": "", "audio_path": None}
 
 def main():
-    st.title("📰 News Summarization and Analysis")
-    
-    # Sidebar
-    st.sidebar.header("Settings")
-    
-    # Company name input
-    company_name = st.text_input("Enter Company Name", "")
-    
-    if company_name:
-        with st.spinner("Analyzing company news..."):
-            data = process_company(company_name)
-            
-            # Display results
-            if data["articles"]:
-                st.subheader("📊 Analysis Results")
+    st.title("News Summarization App")
+    st.write("Analyze news articles and get sentiment analysis for any company.")
+
+    # User input
+    company_name = st.text_input("Enter company name:", "Tesla")
+
+    if st.button("Analyze"):
+        with st.spinner("Analyzing news articles..."):
+            try:
+                # Process company data
+                data = analyze_company_data(company_name)
+                
+                if not data["articles"]:
+                    st.error("No articles found for analysis.")
+                    return
+                
+                # Display overall sentiment
+                st.subheader("Overall Sentiment Analysis")
+                st.write(data["final_sentiment_analysis"])
+                
+                # Create DataFrame for sentiment scores
+                sentiment_df = pd.DataFrame(data["comparative_sentiment_score"])
+                
+                # Display sentiment distribution by source
+                st.subheader("Sentiment Distribution by Source")
+                
+                # Convert sentiment labels to numeric values for visualization
+                sentiment_map = {'positive': 1, 'neutral': 0, 'negative': -1}
+                numeric_df = sentiment_df.replace(sentiment_map)
+                
+                # Calculate sentiment distribution
+                sentiment_dist = numeric_df.apply(lambda x: x.value_counts(normalize=True).to_dict())
+                
+                # Create a new DataFrame for visualization
+                viz_data = []
+                for source in sentiment_df.columns:
+                    dist = sentiment_dist[source]
+                    for sentiment, percentage in dist.items():
+                        viz_data.append({
+                            'Source': source,
+                            'Sentiment': sentiment,
+                            'Percentage': percentage * 100
+                        })
+                
+                viz_df = pd.DataFrame(viz_data)
+                
+                # Create stacked bar chart
+                fig = px.bar(viz_df, 
+                           x='Source', 
+                           y='Percentage',
+                           color='Sentiment',
+                           title='Sentiment Distribution by Source',
+                           barmode='stack')
+                
+                fig.update_layout(
+                    yaxis_title='Percentage',
+                    xaxis_title='News Source',
+                    legend_title='Sentiment'
+                )
+                
+                st.plotly_chart(fig)
+                
+                # Display fine-grained sentiment analysis
+                st.subheader("Fine-grained Sentiment Analysis")
+                
+                # Create tabs for different fine-grained analyses
+                tab1, tab2, tab3 = st.tabs(["Financial Sentiment", "Emotional Sentiment", "ESG Sentiment"])
+                
+                with tab1:
+                    st.write("Financial Market Impact Analysis")
+                    financial_data = []
+                    for article in data["articles"]:
+                        if "financial_sentiment" in article:
+                            financial_data.append({
+                                "Article": article["title"],
+                                "Financial Impact": article["financial_sentiment"],
+                                "Confidence": article.get("fine_grained_sentiment", {}).get("models", {}).get("financial", {}).get("confidence", 0)
+                            })
+                    if financial_data:
+                        st.dataframe(pd.DataFrame(financial_data))
+                    else:
+                        st.info("Financial sentiment analysis not available for these articles.")
+                
+                with tab2:
+                    st.write("Emotional Sentiment Analysis")
+                    emotional_data = []
+                    for article in data["articles"]:
+                        if "emotional_sentiment" in article:
+                            emotional_data.append({
+                                "Article": article["title"],
+                                "Emotional Impact": article["emotional_sentiment"],
+                                "Confidence": article.get("fine_grained_sentiment", {}).get("models", {}).get("emotion", {}).get("confidence", 0)
+                            })
+                    if emotional_data:
+                        st.dataframe(pd.DataFrame(emotional_data))
+                    else:
+                        st.info("Emotional sentiment analysis not available for these articles.")
                 
-                # Display sentiment analysis
-                if data["final_sentiment_analysis"]:
-                    st.write("Sentiment Analysis:", data["final_sentiment_analysis"])
+                with tab3:
+                    st.write("ESG (Environmental, Social, Governance) Analysis")
+                    esg_data = []
+                    for article in data["articles"]:
+                        if "esg_sentiment" in article:
+                            esg_data.append({
+                                "Article": article["title"],
+                                "ESG Impact": article["esg_sentiment"],
+                                "Confidence": article.get("fine_grained_sentiment", {}).get("models", {}).get("esg", {}).get("confidence", 0)
+                            })
+                    if esg_data:
+                        st.dataframe(pd.DataFrame(esg_data))
+                    else:
+                        st.info("ESG sentiment analysis not available for these articles.")
                 
-                # Display articles
-                st.subheader("📰 Recent Articles")
+                # Display articles with detailed sentiment analysis
+                st.subheader("Recent Articles")
                 for article in data["articles"]:
                     with st.expander(article["title"]):
-                        st.write(article["summary"])
-                        st.write("Source:", article["source"])
-                        st.write("Sentiment:", article["sentiment"])
+                        st.write(f"**Source:** {article['source']}")
+                        st.write(f"**Summary:** {article['summary']}")
+                        st.write(f"**Overall Sentiment:** {article['sentiment']}")
+                        
+                        # Display fine-grained sentiment if available
+                        fine_grained = article.get("fine_grained_sentiment", {})
+                        if fine_grained:
+                            st.write("**Fine-grained Analysis:**")
+                            for model_name, model_data in fine_grained.get("models", {}).items():
+                                st.write(f"- {model_name.title()}: {model_data.get('category', 'N/A')} (Confidence: {model_data.get('confidence', 0):.2f})")
+                        
+                        # Display sentiment indices if available
+                        indices = article.get("sentiment_indices", {})
+                        if indices:
+                            st.write("**Sentiment Indices:**")
+                            for index_name, value in indices.items():
+                                st.write(f"- {index_name.replace('_', ' ').title()}: {value:.2f}")
+                        
+                        st.write(f"**URL:** [{article['url']}]({article['url']})")
                 
-                # Display audio if available
-                if data.get("audio_path"):
+                # Display audio player if audio is available
+                if data.get("audio_path") and os.path.exists(data["audio_path"]):
+                    st.subheader("Hindi Audio Summary")
                     st.audio(data["audio_path"])
                 
-                # Display visualizations
-                if data.get("comparative_sentiment_score"):
-                    st.subheader("📈 Sentiment Distribution")
-                    sentiment_df = pd.DataFrame(data["comparative_sentiment_score"])
-                    fig = px.bar(sentiment_df, title="Sentiment Analysis by Source")
-                    st.plotly_chart(fig)
-            else:
-                st.warning("No articles found for this company.")
+            except Exception as e:
+                st.error(f"Error analyzing company data: {str(e)}")
+                print(f"Error: {str(e)}")
 
 if __name__ == "__main__":
     main()