Upload app.py

app.py

@@ -0,0 +1,694 @@
+import streamlit as st
+import pandas as pd
+import requests
+import json
+import base64
+import plotly.express as px
+import plotly.graph_objects as go
+import os
+from io import BytesIO
+from datetime import datetime
+
+# Set page configuration
+st.set_page_config(
+    page_title="News Summarization & Analysis",
+    page_icon="📰",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# API endpoint (Flask backend)
+API_URL = "http://0.0.0.0:8000"
+
+def get_company_news(company_name):
+    """Fetch news articles for a given company via API"""
+    try:
+        response = requests.get(f"{API_URL}/news/{company_name}")
+        if response.status_code == 200:
+            return response.json()
+        else:
+            st.error(f"Error fetching news: {response.text}")
+            return None
+    except Exception as e:
+        st.error(f"API connection error: {str(e)}")
+        return None
+
+def get_analysis(company_name, articles):
+    """Get sentiment analysis and comparative analysis via API"""
+    try:
+        response = requests.post(
+            f"{API_URL}/analyze", 
+            json={
+                "company": company_name,
+                "articles": articles
+            }
+        )
+        if response.status_code == 200:
+            return response.json()
+        else:
+            st.error(f"Error analyzing content: {response.text}")
+            return None
+    except Exception as e:
+        st.error(f"API connection error: {str(e)}")
+        return None
+
+def get_tts(text, language='hi'):
+    """Get TTS audio in the specified language via API"""
+    try:
+        response = requests.post(
+            f"{API_URL}/tts", 
+            json={
+                "text": text,
+                "language": language
+            }
+        )
+        if response.status_code == 200:
+            return response.content, language
+        else:
+            st.error(f"Error generating speech: {response.text}")
+            return None, language
+    except Exception as e:
+        st.error(f"API connection error: {str(e)}")
+        return None, language
+
+def create_audio_player(audio_bytes):
+    """Create an HTML audio player for the TTS audio"""
+    audio_base64 = base64.b64encode(audio_bytes).decode()
+    audio_html = f"""
+        <audio controls>
+            <source src="data:audio/mp3;base64,{audio_base64}" type="audio/mp3">
+            Your browser does not support the audio element.
+        </audio>
+    """
+    return audio_html
+
+def display_article_details(articles):
+    """Display detailed information about each article in a card layout"""
+    st.markdown("""
+    <style>
+    .article-card {
+        background-color: #f9f9f9;
+        border-radius: 10px;
+        padding: 20px;
+        margin-bottom: 20px;
+        border-left: 5px solid #4CAF50;
+        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+    }
+    .article-negative {
+        border-left: 5px solid #F44336;
+    }
+    .article-neutral {
+        border-left: 5px solid #9E9E9E;
+    }
+    .article-title {
+        font-size: 18px;
+        font-weight: bold;
+        margin-bottom: 10px;
+    }
+    .article-meta {
+        color: #666;
+        font-size: 14px;
+        margin-bottom: 10px;
+    }
+    .article-summary {
+        margin-bottom: 15px;
+    }
+    .article-sentiment {
+        display: inline-block;
+        padding: 5px 10px;
+        border-radius: 20px;
+        font-size: 14px;
+        margin-right: 10px;
+    }
+    .sentiment-positive {
+        background-color: rgba(76, 175, 80, 0.2);
+        color: #2E7D32;
+    }
+    .sentiment-negative {
+        background-color: rgba(244, 67, 54, 0.2);
+        color: #C62828;
+    }
+    .sentiment-neutral {
+        background-color: rgba(158, 158, 158, 0.2);
+        color: #616161;
+    }
+    .topic-tag {
+        display: inline-block;
+        background-color: #E0E0E0;
+        padding: 3px 10px;
+        border-radius: 15px;
+        margin-right: 8px;
+        margin-bottom: 8px;
+        font-size: 12px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+    
+    cols = st.columns(1)
+    
+    for i, article in enumerate(articles):
+        sentiment = article['Sentiment']
+        sentiment_class = ""
+        tag_class = ""
+        
+        if sentiment == "Positive":
+            sentiment_class = "article-positive"
+            tag_class = "sentiment-positive"
+        elif sentiment == "Negative":
+            sentiment_class = "article-negative"
+            tag_class = "sentiment-negative"
+        else:
+            sentiment_class = "article-neutral"
+            tag_class = "sentiment-neutral"
+        
+        article_html = f"""
+        <div class="article-card {sentiment_class}">
+            <div class="article-title">{article['Title']}</div>
+            <div class="article-meta">
+                Source: {article.get('Source', 'Unknown')} | 
+                Date: {article.get('Date', 'N/A')}
+            </div>
+            <div class="article-summary">{article['Summary']}</div>
+            <div class="article-sentiment {tag_class}">{sentiment}</div>
+        """
+        
+        # Add topics as tags
+        if 'Topics' in article and article['Topics']:
+            article_html += '<div class="article-topics">'
+            for topic in article['Topics']:
+                article_html += f'<span class="topic-tag">{topic}</span>'
+            article_html += '</div>'
+        
+        article_html += f"""
+            <div style="margin-top: 10px;">
+                <a href="{article.get('URL', '#')}" target="_blank" style="text-decoration: none;">
+                    <span style="color: #1E88E5;">Read original article →</span>
+                </a>
+            </div>
+        </div>
+        """
+        
+        cols[0].markdown(article_html, unsafe_allow_html=True)
+
+def display_sentiment_distribution(analysis):
+    """Display sentiment distribution chart with enhanced styling"""
+    if 'Comparative Sentiment Score' in analysis and 'Sentiment Distribution' in analysis['Comparative Sentiment Score']:
+        dist = analysis['Comparative Sentiment Score']['Sentiment Distribution']
+        data = {
+            'Sentiment': list(dist.keys()),
+            'Count': list(dist.values())
+        }
+        df = pd.DataFrame(data)
+        
+        # Create color map
+        color_map = {
+            'Positive': '#4CAF50', 
+            'Negative': '#F44336', 
+            'Neutral': '#9E9E9E'
+        }
+        
+        # Create a card container for the chart
+        st.markdown("""
+        <div style="background-color:white; padding:20px; border-radius:10px; box-shadow:0 4px 6px rgba(0,0,0,0.1); margin-bottom:20px;">
+            <h3 style="margin-bottom:15px; border-bottom:1px solid #eee; padding-bottom:10px;">Sentiment Distribution</h3>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        # Create pie chart for sentiment distribution
+        labels = list(dist.keys())
+        values = list(dist.values())
+        colors = [color_map[label] for label in labels]
+        
+        # Create two columns for different chart types
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            # Bar chart
+            fig_bar = px.bar(
+                df, 
+                x='Sentiment', 
+                y='Count',
+                color='Sentiment',
+                color_discrete_map=color_map,
+                title="Sentiment Distribution (Bar Chart)"
+            )
+            fig_bar.update_layout(
+                plot_bgcolor='rgba(0,0,0,0)',
+                paper_bgcolor='rgba(0,0,0,0)',
+                font=dict(size=14),
+                margin=dict(l=20, r=20, t=40, b=20),
+                height=350
+            )
+            st.plotly_chart(fig_bar, use_container_width=True)
+            
+        with col2:
+            # Pie chart
+            fig_pie = go.Figure(data=[go.Pie(
+                labels=labels, 
+                values=values,
+                marker=dict(colors=colors),
+                textinfo='percent+label',
+                hole=.4
+            )])
+            fig_pie.update_layout(
+                title_text="Sentiment Distribution (Pie Chart)",
+                annotations=[dict(text='Sentiment', x=0.5, y=0.5, font_size=14, showarrow=False)],
+                plot_bgcolor='rgba(0,0,0,0)',
+                paper_bgcolor='rgba(0,0,0,0)',
+                font=dict(size=14),
+                margin=dict(l=20, r=20, t=40, b=20),
+                height=350
+            )
+            st.plotly_chart(fig_pie, use_container_width=True)
+            
+        # Add a summary of the sentiment distribution
+        total = sum(values)
+        if total > 0:
+            percentages = {label: (count/total*100) for label, count in zip(labels, values)}
+            
+            # Create a summary card
+            summary_html = """
+            <div style="background-color:#f8f9fa; padding:15px; border-radius:8px; margin-top:10px;">
+                <h4 style="margin-bottom:10px;">Summary</h4>
+                <p style="font-size:15px; line-height:1.5;">
+            """
+            
+            for label in labels:
+                if label in percentages:
+                    color = color_map[label]
+                    summary_html += f'<span style="color:{color}; font-weight:bold;">{label}</span>: {percentages[label]:.1f}% | '
+            
+            summary_html = summary_html.rstrip(' | ') + '</p></div>'
+            st.markdown(summary_html, unsafe_allow_html=True)
+
+def display_topic_analysis(analysis):
+    """Display topic analysis visualization"""
+    if 'Comparative Sentiment Score' in analysis and 'Topic Overlap' in analysis['Comparative Sentiment Score']:
+        topic_data = analysis['Comparative Sentiment Score']['Topic Overlap']
+        
+        # Prepare data for visualization
+        all_topics = set()
+        if 'Common Topics' in topic_data:
+            all_topics.update(topic_data['Common Topics'])
+        
+        for i in range(1, 11):  # Check for unique topics in each article
+            key = f"Unique Topics in Article {i}"
+            if key in topic_data and topic_data[key]:
+                all_topics.update(topic_data[key])
+        
+        # Count topic occurrences across articles
+        topic_counts = {}
+        for topic in all_topics:
+            count = 0
+            if 'Common Topics' in topic_data and topic in topic_data['Common Topics']:
+                count += len(analysis['Articles'])  # All articles have common topics
+            
+            for i in range(1, 11):
+                key = f"Unique Topics in Article {i}"
+                if key in topic_data and topic in topic_data[key]:
+                    count += 1
+                    
+            topic_counts[topic] = count
+            
+        # Create DataFrame and visualization
+        topic_df = pd.DataFrame({
+            'Topic': list(topic_counts.keys()),
+            'Occurrence': list(topic_counts.values())
+        }).sort_values('Occurrence', ascending=False)
+        
+        fig = px.bar(
+            topic_df,
+            x='Topic',
+            y='Occurrence',
+            title="Topic Distribution Across Articles",
+            color='Occurrence',
+            color_continuous_scale=px.colors.sequential.Viridis
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+def display_comparative_analysis(analysis):
+    """Display comparative analysis details"""
+    if 'Comparative Sentiment Score' in analysis and 'Coverage Differences' in analysis['Comparative Sentiment Score']:
+        differences = analysis['Comparative Sentiment Score']['Coverage Differences']
+        
+        st.subheader("Comparative Analysis")
+        for i, diff in enumerate(differences):
+            with st.expander(f"Comparison {i+1}"):
+                st.write(f"**Comparison**: {diff['Comparison']}")
+                st.write(f"**Impact**: {diff['Impact']}")
+
+# Main app layout with enhanced design and better readability
+st.markdown("""
+<style>
+    .main-header {
+        text-align: center;
+        padding: 2rem 0;
+        background: linear-gradient(to right, #2E7D32, #1565C0);
+        color: white;
+        border-radius: 10px;
+        margin-bottom: 30px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+    }
+    .app-title {
+        font-size: 36px;
+        font-weight: bold;
+        text-shadow: 1px 1px 3px rgba(0,0,0,0.3);
+        margin-bottom: 15px;
+    }
+    .app-description {
+        font-size: 18px;
+        color: white;
+        max-width: 800px;
+        margin: 0 auto;
+        line-height: 1.6;
+        text-shadow: 0px 1px 2px rgba(0,0,0,0.2);
+    }
+    .benefits-container {
+        display: flex;
+        justify-content: center;
+        gap: 20px;
+        margin-top: 20px;
+        flex-wrap: wrap;
+    }
+    .benefit-item {
+        background-color: rgba(255,255,255,0.25);
+        padding: 8px 15px;
+        border-radius: 20px;
+        font-size: 14px;
+        font-weight: 500;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        text-shadow: 0px 1px 1px rgba(0,0,0,0.1);
+    }
+</style>
+
+<div class="main-header">
+    <div class="app-title">📰 News Summarization & Sentiment Analysis</div>
+    <p class="app-description">
+        Analyze recent news articles about any company. Get sentiment analysis, topic extraction, 
+        and multilingual text-to-speech summaries instantly in 10 different languages.
+    </p>
+    <div class="benefits-container">
+        <div class="benefit-item">✅ Real-time News Analysis</div>
+        <div class="benefit-item">📊 Sentiment Visualization</div>
+        <div class="benefit-item">🔍 Topic Extraction</div>
+        <div class="benefit-item">🎧 Multilingual Text-to-Speech</div>
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+# Input form with enhanced styling
+st.markdown("""
+<style>
+    .search-container {
+        background-color: white;
+        padding: 25px;
+        border-radius: 10px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+        margin-bottom: 30px;
+    }
+    .search-title {
+        font-size: 20px;
+        font-weight: bold;
+        margin-bottom: 15px;
+        color: #333;
+    }
+    .search-description {
+        color: #666;
+        margin-bottom: 20px;
+        font-size: 16px;
+    }
+</style>
+<div class="search-container">
+    <div class="search-title">🔍 Search for Company News</div>
+    <div class="search-description">
+        Enter a company name below to analyze its recent news coverage.
+        Try companies like Tesla, Apple, Microsoft, Google, or Amazon.
+    </div>
+</div>
+""", unsafe_allow_html=True)
+
+with st.form("search_form"):
+    col1, col2 = st.columns([3, 1])
+    with col1:
+        company_name = st.text_input("Company Name", placeholder="Enter company name (e.g., Tesla)", label_visibility="collapsed")
+    with col2:
+        submit_button = st.form_submit_button("🔍 Analyze News")
+    
+    # Add some example buttons below the form
+    st.markdown("""
+    <style>
+        .example-row {
+            display: flex;
+            gap: 10px;
+            margin-top: 10px;
+            flex-wrap: wrap;
+            justify-content: center;
+        }
+        .example-chip {
+            background-color: #f0f2f6;
+            border-radius: 20px;
+            padding: 5px 15px;
+            font-size: 12px;
+            cursor: pointer;
+            transition: all 0.2s;
+        }
+        .example-chip:hover {
+            background-color: #4CAF50;
+            color: white;
+        }
+    </style>
+    <div style="text-align: center; margin-top: 10px; font-size: 12px; color: #666;">
+        Try analyzing news for:
+        <div class="example-row">
+            <div class="example-chip">Tesla</div>
+            <div class="example-chip">Apple</div>
+            <div class="example-chip">Microsoft</div>
+            <div class="example-chip">Google</div>
+            <div class="example-chip">Amazon</div>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+# Process form submission
+if submit_button and company_name:
+    with st.spinner(f"Fetching news articles about {company_name}..."):
+        articles_data = get_company_news(company_name)
+        
+    if articles_data and 'articles' in articles_data and len(articles_data['articles']) > 0:
+        articles = articles_data['articles']
+        
+        with st.spinner("Performing sentiment analysis..."):
+            analysis_result = get_analysis(company_name, articles)
+            
+        if analysis_result:
+            # Store complete analysis in session state
+            st.session_state.analysis = analysis_result
+            
+            # Display summary and stats
+            st.header(f"Analysis Results for {company_name}")
+            
+            # Create a nice header with company logo or icon
+            company_icon = "🏢"  # Default company icon
+            if company_name.lower() == "tesla":
+                company_icon = "🚗"
+            elif company_name.lower() == "apple":
+                company_icon = "🍎"
+            elif company_name.lower() == "microsoft":
+                company_icon = "💻"
+            elif company_name.lower() == "amazon":
+                company_icon = "📦"
+            elif company_name.lower() == "google":
+                company_icon = "🔍"
+                
+            st.markdown(f"""
+            <div style="background-color:#f0f2f6; padding:20px; border-radius:10px; margin-bottom:20px;">
+                <h1 style="text-align:center; margin-bottom:20px;">{company_icon} {company_name} News Analysis</h1>
+                <p style="text-align:center; font-size:16px; color:#666;">
+                    Analysis of {len(analysis_result['Articles'])} news articles | Generated on {datetime.now().strftime('%B %d, %Y')}
+                </p>
+            </div>
+            """, unsafe_allow_html=True)
+            
+            # Display visualization tabs with custom styling
+            st.markdown("""
+            <style>
+            .stTabs [data-baseweb="tab-list"] {
+                gap: 8px;
+            }
+            .stTabs [data-baseweb="tab"] {
+                border-radius: 4px 4px 0px 0px;
+                padding: 10px 16px;
+                background-color: #f0f2f6;
+            }
+            .stTabs [aria-selected="true"] {
+                background-color: #4CAF50 !important;
+                color: white !important;
+            }
+            </style>
+            """, unsafe_allow_html=True)
+            
+            tab1, tab2, tab3, tab4 = st.tabs(["📊 Overview", "😊 Sentiment Analysis", "🔍 Topic Analysis", "📰 Article Details"])
+            
+            with tab1:
+                # Create a card-style container for the summary
+                st.markdown("""
+                <div style="background-color:white; padding:25px; border-radius:10px; box-shadow:0 4px 6px rgba(0,0,0,0.1); margin-bottom:20px;">
+                    <h3 style="margin-bottom:15px; border-bottom:1px solid #eee; padding-bottom:10px;">Executive Summary</h3>
+                """, unsafe_allow_html=True)
+                
+                summary_text = analysis_result.get("Final Sentiment Analysis", "No summary available")
+                st.markdown(f"<p style='font-size:16px; line-height:1.6;'>{summary_text}</p>", unsafe_allow_html=True)
+                
+                st.markdown("</div>", unsafe_allow_html=True)
+                
+                if "Final Sentiment Analysis" in analysis_result:
+                    # Language selection for TTS
+                    language_options = {
+                        "hi": "Hindi",
+                        "en": "English",
+                        "es": "Spanish",
+                        "fr": "French",
+                        "de": "German",
+                        "ja": "Japanese",
+                        "zh-CN": "Chinese",
+                        "ru": "Russian",
+                        "ar": "Arabic",
+                        "it": "Italian"
+                    }
+                    
+                    selected_language = st.selectbox(
+                        "Select Language for Text-to-Speech",
+                        options=list(language_options.keys()),
+                        format_func=lambda x: language_options[x],
+                        index=0  # Default to Hindi
+                    )
+                    
+                    with st.spinner(f"Generating {language_options[selected_language]} text-to-speech..."):
+                        audio_bytes, language = get_tts(analysis_result["Final Sentiment Analysis"], selected_language)
+                    
+                    if audio_bytes:
+                        st.markdown(f"""
+                        <div style="background-color:white; padding:25px; border-radius:10px; box-shadow:0 4px 6px rgba(0,0,0,0.1);">
+                            <h3 style="margin-bottom:15px; border-bottom:1px solid #eee; padding-bottom:10px;">
+                                <span style="vertical-align:middle;">🔊</span> {language_options[language]} Text-to-Speech Summary
+                            </h3>
+                        """, unsafe_allow_html=True)
+                        
+                        st.markdown(create_audio_player(audio_bytes), unsafe_allow_html=True)
+                        st.markdown("</div>", unsafe_allow_html=True)
+            
+            with tab2:
+                st.subheader("Sentiment Distribution")
+                display_sentiment_distribution(analysis_result)
+                display_comparative_analysis(analysis_result)
+                
+            with tab3:
+                st.subheader("Topic Analysis")
+                display_topic_analysis(analysis_result)
+                
+            with tab4:
+                st.subheader("Article Details")
+                display_article_details(analysis_result['Articles'])
+
+            # Add video download tab
+            tab5 = st.tabs(["📹 Demo Video"])[0]
+            with tab5:
+                st.subheader("Record and Download Demo")
+                if st.button("Record Demo (30s)"):
+                    with st.spinner("Recording demo video..."):
+                        try:
+                            from record_demo import record_demo
+                            record_demo()
+                            st.success("Recording completed!")
+                            
+                            # Display download button
+                            with open('demo_video.mp4', 'rb') as video_file:
+                                video_bytes = video_file.read()
+                                st.download_button(
+                                    label="Download Demo Video",
+                                    data=video_bytes,
+                                    file_name="company_analysis_demo.mp4",
+                                    mime="video/mp4"
+                                )
+                        except Exception as e:
+                            st.error(f"Error recording video: {str(e)}")
+                
+            # Display JSON output option
+            st.subheader("Raw JSON Output")
+            with st.expander("Show JSON"):
+                st.json(analysis_result)
+                
+        else:
+            st.error("Failed to perform analysis. Please try again.")
+    else:
+        st.warning(f"No news articles found for {company_name}. Please try another company name.")
+
+# Footer with enhanced design and improved readability
+st.markdown("""
+<style>
+    .footer {
+        background: linear-gradient(to right, #2E7D32, #1565C0);
+        padding: 30px 10px;
+        color: white;
+        border-radius: 10px;
+        text-align: center;
+        margin-top: 40px;
+        box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+    }
+    .footer-content {
+        max-width: 800px;
+        margin: 0 auto;
+    }
+    .footer-title {
+        font-size: 22px;
+        margin-bottom: 15px;
+        font-weight: bold;
+        text-shadow: 1px 1px 2px rgba(0,0,0,0.3);
+    }
+    .footer-text {
+        font-size: 15px;
+        line-height: 1.6;
+        margin-bottom: 15px;
+        text-shadow: 0px 1px 1px rgba(0,0,0,0.2);
+    }
+    .footer-features {
+        display: flex;
+        justify-content: center;
+        gap: 15px;
+        margin: 20px 0;
+        flex-wrap: wrap;
+    }
+    .footer-feature {
+        background-color: rgba(255,255,255,0.25);
+        padding: 8px 15px;
+        border-radius: 20px;
+        font-size: 14px;
+        font-weight: 500;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+        text-shadow: 0px 1px 1px rgba(0,0,0,0.1);
+    }
+    .footer-copyright {
+        margin-top: 15px;
+        font-size: 14px;
+        opacity: 0.9;
+        text-shadow: 0px 1px 1px rgba(0,0,0,0.1);
+    }
+</style>
+
+<div class="footer">
+    <div class="footer-content">
+        <div class="footer-title">📰 News Summarization & Analysis Application</div>
+        <div class="footer-text">
+            A powerful tool for analyzing news content, extracting sentiments, and generating insights.
+            Get comprehensive analysis of any company's news coverage within seconds.
+        </div>
+        <div class="footer-features">
+            <div class="footer-feature">⚡ Real-time News Extraction</div>
+            <div class="footer-feature">📊 Sentiment Analysis</div>
+            <div class="footer-feature">🔍 Topic Analysis</div>
+            <div class="footer-feature">🎧 Multilingual Text-to-Speech</div>
+        </div>
+        <div class="footer-copyright">Created with Streamlit • {datetime.now().year}</div>
+    </div>
+</div>
+""", unsafe_allow_html=True)