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| """Streamlit frontend for the News Summarization application.""" | |
| import streamlit as st | |
| import pandas as pd | |
| import json | |
| import os | |
| import plotly.express as px | |
| import altair as alt | |
| from utils import analyze_company_data # Import the analysis function directly | |
| # Set page config | |
| st.set_page_config( | |
| page_title="News Summarization App", | |
| page_icon="📰", | |
| layout="wide" | |
| ) | |
| # Show loading message | |
| with st.spinner("Initializing the application... Please wait while we load the models."): | |
| # Initialize components | |
| try: | |
| from utils import NewsExtractor, SentimentAnalyzer, TextSummarizer, TextToSpeechConverter | |
| st.success("Application initialized successfully!") | |
| except Exception as e: | |
| st.error(f"Error initializing application: {str(e)}") | |
| st.info("Please try refreshing the page.") | |
| def process_company(company_name): | |
| """Process company data directly.""" | |
| try: | |
| # Call the analysis function directly from utils | |
| data = analyze_company_data(company_name) | |
| # Generate 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) | |
| data['audio_path'] = audio_path | |
| 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 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"] | |
| }) | |
| 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"] | |
| }) | |
| if emotional_data: | |
| st.dataframe(pd.DataFrame(emotional_data)) | |
| else: | |
| st.info("Emotional sentiment analysis not available for these articles.") | |
| 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"] | |
| }) | |
| if esg_data: | |
| st.dataframe(pd.DataFrame(esg_data)) | |
| else: | |
| st.info("ESG sentiment analysis not available for these articles.") | |
| # Display articles with detailed sentiment analysis | |
| st.subheader("Recent Articles") | |
| for article in data["articles"]: | |
| with st.expander(article["title"]): | |
| 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 | |
| if "financial_sentiment" in article: | |
| st.write(f"**Financial Impact:** {article['financial_sentiment']}") | |
| if "emotional_sentiment" in article: | |
| st.write(f"**Emotional Impact:** {article['emotional_sentiment']}") | |
| if "esg_sentiment" in article: | |
| st.write(f"**ESG Impact:** {article['esg_sentiment']}") | |
| st.write(f"**URL:** [{article['url']}]({article['url']})") | |
| except Exception as e: | |
| st.error(f"Error analyzing company data: {str(e)}") | |
| print(f"Error: {str(e)}") | |
| if __name__ == "__main__": | |
| main() | |