Update app.py

app.py

@@ -1,496 +1,388 @@
-import streamlit as st
-import requests
-import pandas as pd
-import json
-import os
-import matplotlib.pyplot as plt
-import seaborn as sns
-import base64
-from io import BytesIO
-from PIL import Image, ImageEnhance
-import time
-from typing import Dict, Any, List
-
-# API Base URL - Change this to match your deployment
-API_BASE_URL = "http://localhost:8000"
-
-# New function to generate the example output format
-def generate_example_output(company_name: str) -> str:
-    """
-    Generate output in the example format for the given company.
-    Returns the formatted JSON as a string.
-    """
-    try:
-        # Make API request to get the analysis data
-        url = f"{API_BASE_URL}/api/complete_analysis"
-        response = requests.post(url, json={"company_name": company_name})
-        response.raise_for_status()
-        data = response.json()
-        
-        # Format the data to match the example output format exactly
-        formatted_output = {
-            "Company": data["Company"],
-            "Articles": data["Articles"],
-            "Comparative Sentiment Score": {
-                "Sentiment Distribution": data["Comparative Sentiment Score"]["Sentiment Distribution"],
-                "Coverage Differences": data["Comparative Sentiment Score"]["Coverage Differences"],
-                "Topic Overlap": data["Comparative Sentiment Score"]["Topic Overlap"]
-            },
-            "Final Sentiment Analysis": data["Final Sentiment Analysis"],
-            "Audio": "[Play Hindi Speech]" if data.get("Audio") else "No audio available"
-        }
-        
-        # Convert to JSON string with proper formatting
-        return json.dumps(formatted_output, indent=2)
-        
-    except Exception as e:
-        return json.dumps({
-            "error": str(e),
-            "message": "Failed to generate example output"
-        }, indent=2)
-
-# Function to run in terminal mode
-def run_terminal_mode():
-    """Run the app in terminal mode to output JSON"""
-    print("News Analysis Terminal Mode")
-    company_name = input("Enter company name: ")
-    print(f"Analyzing {company_name}...")
-    output = generate_example_output(company_name)
-    print(output)
-
-# Check if run directly or imported
-if __name__ == "__main__":
-    # Check if terminal mode is requested via command line args
-    import sys
-    if len(sys.argv) > 1 and sys.argv[1] == "--terminal":
-        run_terminal_mode()
-    else:
-        # Continue with the Streamlit app
-        
-        # App title and description
-        st.set_page_config(
-            page_title="News Summarization & TTS",
-            page_icon="📰",
-            layout="wide",
-            initial_sidebar_state="expanded"
-        )
-
-# Custom CSS for better UI
-st.markdown("""
-<style>
-    .main-header {
-        font-size: 2.5rem;
-        font-weight: 700;
-        color: #1E3A8A;
-        margin-bottom: 1rem;
-    }
-    .sub-header {
-        font-size: 1.5rem;
-        font-weight: 600;
-        color: #2563EB;
-        margin-top: 1rem;
-        margin-bottom: 0.5rem;
-    }
-    .card {
-        padding: 1.5rem;
-        border-radius: 0.5rem;
-        background-color: #F8FAFC;
-        border: 1px solid #E2E8F0;
-        margin-bottom: 1rem;
-    }
-    .positive {
-        color: #059669;
-        font-weight: 600;
-    }
-    .negative {
-        color: #DC2626;
-        font-weight: 600;
-    }
-    .neutral {
-        color: #6B7280;
-        font-weight: 600;
-    }
-    .topic-tag {
-        display: inline-block;
-        padding: 0.25rem 0.5rem;
-        border-radius: 2rem;
-        background-color: #E5E7EB;
-        color: #1F2937;
-        font-size: 0.75rem;
-        margin-right: 0.5rem;
-        margin-bottom: 0.5rem;
-    }
-    .audio-container {
-        width: 100%;
-        padding: 1rem;
-        background-color: #F3F4F6;
-        border-radius: 0.5rem;
-        margin-top: 1rem;
-    }
-    .info-text {
-        font-size: 0.9rem;
-        color: #4B5563;
-    }
-    .article-title {
-        font-size: 1.2rem;
-        font-weight: 600;
-        color: #111827;
-        margin-bottom: 0.5rem;
-        margin-top: 0.5rem;
-    }
-    .article-summary {
-        font-size: 0.9rem;
-        color: #374151;
-        margin-bottom: 0.5rem;
-    }
-    .article-meta {
-        font-size: 0.8rem;
-        color: #6B7280;
-        margin-bottom: 0.5rem;
-    }
-    .section-divider {
-        height: 1px;
-        background-color: #E5E7EB;
-        margin: 1.5rem 0;
-    }
-    .chart-container {
-        background-color: white;
-        padding: 1rem;
-        border-radius: 0.5rem;
-        border: 1px solid #E2E8F0;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Function to make API requests
-def make_api_request(endpoint: str, data: Dict[str, Any] = None, method: str = "POST") -> Dict[str, Any]:
-    """Make API request to the backend."""
-    url = f"{API_BASE_URL}{endpoint}"
-    
-    try:
-        if method == "GET":
-            response = requests.get(url)
-        else:
-            response = requests.post(url, json=data)
-            
-        response.raise_for_status()
-        return response.json()
-    except requests.exceptions.ConnectionError:
-        st.error("⚠️ Connection Error: Cannot connect to the API server. Please ensure the API server is running at " + API_BASE_URL)
-        return {}
-    except requests.exceptions.Timeout:
-        st.error("⚠️ Timeout Error: The request took too long to complete. Please try again with a different company name.")
-        return {}
-    except requests.exceptions.HTTPError as e:
-        if e.response.status_code == 404:
-            st.error("⚠️ No articles found for this company. Please try another company name.")
-        elif e.response.status_code == 500:
-            # Try to get detailed error message
-            try:
-                error_detail = e.response.json().get("detail", "Unknown server error")
-                st.error(f"⚠️ Server Error: {error_detail}")
-            except:
-                st.error("⚠️ Internal Server Error: Something went wrong on the server. Please try again later.")
-        else:
-            st.error(f"⚠️ HTTP Error: {str(e)}")
-        return {}
-    except Exception as e:
-        st.error(f"⚠️ Error: {str(e)}")
-        return {}
-
-# Function to create sentiment color
-def get_sentiment_color(sentiment: str) -> str:
-    """Return CSS class for sentiment."""
-    if sentiment == "Positive":
-        return "positive"
-    elif sentiment == "Negative":
-        return "negative"
-    else:
-        return "neutral"
-
-# Function to create visualization for sentiment distribution
-def plot_sentiment_distribution(sentiment_data: Dict[str, int]):
-    """Create and display a bar chart for sentiment distribution."""
-    labels = ["Positive", "Neutral", "Negative"]
-    values = [sentiment_data[label] for label in labels]
-    colors = ["#059669", "#6B7280", "#DC2626"]
-    
-    fig, ax = plt.subplots(figsize=(10, 6))
-    ax.bar(labels, values, color=colors)
-    ax.set_title("Sentiment Distribution", fontsize=16, fontweight='bold')
-    ax.set_ylabel("Number of Articles", fontsize=12)
-    ax.grid(axis='y', linestyle='--', alpha=0.7)
-    
-    # Add value labels on top of bars
-    for i, v in enumerate(values):
-        ax.text(i, v + 0.1, str(v), ha='center', fontweight='bold')
-    
-    return fig
-
-# Function to display article information
-def display_article(article: Dict[str, Any], index: int):
-    """Display article information in a card layout."""
-    st.markdown(f"<div class='card'>", unsafe_allow_html=True)
-    
-    # Article title and sentiment
-    sentiment = article.get("Sentiment", "Neutral")
-    sentiment_class = get_sentiment_color(sentiment)
-    
-    st.markdown(f"<h3 class='article-title'>{index+1}. {article['Title']}</h3>", unsafe_allow_html=True)
-    st.markdown(f"<span class='{sentiment_class}'>{sentiment}</span>", unsafe_allow_html=True)
-    
-    # Article summary
-    st.markdown("<div class='article-summary'>", unsafe_allow_html=True)
-    st.markdown(f"{article.get('Summary', 'No summary available.')}", unsafe_allow_html=True)
-    st.markdown("</div>", unsafe_allow_html=True)
-    
-    # Topics
-    if "Topics" in article and article["Topics"]:
-        st.markdown("<div>", unsafe_allow_html=True)
-        for topic in article["Topics"]:
-            st.markdown(f"<span class='topic-tag'>{topic}</span>", unsafe_allow_html=True)
-        st.markdown("</div>", unsafe_allow_html=True)
-    
-    st.markdown("</div>", unsafe_allow_html=True)
-
-# App layout
-st.markdown("<h1 class='main-header'>📰 News Summarization & Text-to-Speech</h1>", unsafe_allow_html=True)
-st.markdown("""
-<p class='info-text'>
-This application extracts news articles about a company, performs sentiment analysis, conducts comparative analysis,
-and generates a text-to-speech output in Hindi. Enter a company name to get started.
-</p>
-""", unsafe_allow_html=True)
-
-# Sidebar
-st.sidebar.image("https://cdn-icons-png.flaticon.com/512/2593/2593073.png", width=100)
-st.sidebar.title("News Analysis Settings")
-
-# Company selection
-company_input_method = st.sidebar.radio(
-    "Select company input method:",
-    options=["Text Input", "Choose from List"]
-)
-
-if company_input_method == "Text Input":
-    company_name = st.sidebar.text_input("Enter Company Name:", placeholder="e.g., Tesla")
-else:
-    companies = ["Apple", "Google", "Microsoft", "Amazon", "Tesla", "Meta", "Netflix", "Uber", "Airbnb", "Twitter"]
-    company_name = st.sidebar.selectbox("Select Company:", companies)
-
-# Analysis settings
-max_articles = st.sidebar.slider("Maximum Articles to Analyze:", min_value=5, max_value=20, value=10)
-st.sidebar.markdown("---")
-
-# Analysis button
-analyze_button = st.sidebar.button("Analyze Company News", type="primary")
-
-# Audio playback settings
-st.sidebar.markdown("## Audio Settings")
-audio_speed = st.sidebar.select_slider("TTS Speech Speed:", options=["Slow", "Normal", "Fast"], value="Normal")
-st.sidebar.markdown("---")
-
-# Add option to see JSON in example format
-st.sidebar.markdown("## Developer Options")
-show_json = st.sidebar.checkbox("Show JSON output in example format")
-st.sidebar.markdown("---")
-
-# About section
-with st.sidebar.expander("About This App"):
-    st.markdown("""
-    This application performs:
-    - News extraction from multiple sources
-    - Sentiment analysis of the content
-    - Topic identification and comparative analysis
-    - Text-to-speech conversion to Hindi
-    
-    Built with Streamlit, FastAPI, and various NLP tools.
-    """)
-
-# Main content area
-if analyze_button and company_name:
-    with st.spinner(f"Analyzing news for {company_name}... This may take a minute"):
-        # Perform complete analysis
-        response = make_api_request(
-            "/api/complete_analysis", 
-            {"company_name": company_name}
-        )
-        
-        if not response:
-            st.error("Failed to retrieve data. Please try again.")
-        elif "detail" in response:
-            st.error(response["detail"])
-        else:
-            # Display company header
-            st.markdown(f"<h2 class='sub-header'>Analysis Results for {response['Company']}</h2>", unsafe_allow_html=True)
-            
-            # Display sentiment summary
-            col1, col2 = st.columns([2, 1])
-            
-            with col1:
-                st.markdown("<div class='card'>", unsafe_allow_html=True)
-                st.markdown("<h3 class='sub-header'>Sentiment Overview</h3>", unsafe_allow_html=True)
-                st.markdown(f"{response['Final Sentiment Analysis']}")
-                st.markdown("</div>", unsafe_allow_html=True)
-            
-            with col2:
-                sentiment_data = response["Comparative Sentiment Score"]["Sentiment Distribution"]
-                fig = plot_sentiment_distribution(sentiment_data)
-                st.pyplot(fig)
-            
-            st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
-            
-            # Display Hindi TTS audio
-            if "Audio" in response and response["Audio"]:
-                st.markdown("<h3 class='sub-header'>Hindi Audio Summary</h3>", unsafe_allow_html=True)
-                
-                audio_message = response["Audio"]
-                
-                if audio_message == "Failed to generate audio":
-                    st.warning("Hindi audio could not be generated. However, you can still read the Hindi text below.")
-                else:
-                    try:
-                        # Check if the response contains the actual audio file path
-                        audio_file_path = response.get("_audio_file_path")
-                        
-                        if audio_file_path:
-                            # Extract the filename
-                            audio_filename = os.path.basename(audio_file_path)
-                            audio_url = f"{API_BASE_URL}/api/audio/{audio_filename}"
-                        else:
-                            # If no path is provided, just display a message
-                            st.info("Audio is available but the path was not provided.")
-                            audio_url = None
-                        
-                        if audio_url:
-                            # Attempt to download the audio file
-                            audio_response = requests.get(audio_url)
-                            if audio_response.status_code == 200:
-                                # Save temporarily
-                                temp_audio_path = f"temp_audio_{os.path.basename(audio_url)}"
-                                with open(temp_audio_path, "wb") as f:
-                                    f.write(audio_response.content)
-                                
-                                # Play from local file
-                                st.markdown("<div class='audio-container'>", unsafe_allow_html=True)
-                                st.audio(temp_audio_path, format="audio/mp3")
-                                
-                                # Display audio download link
-                                st.markdown(f"<a href='{audio_url}' download='hindi_summary.mp3'>Download Hindi Audio</a>", unsafe_allow_html=True)
-                                
-                                # Clean up temp file (optional)
-                                # os.remove(temp_audio_path)  # Uncomment to delete after use
-                            else:
-                                st.warning(f"Unable to load audio file (HTTP {audio_response.status_code}). You can still read the Hindi text below.")
-                        else:
-                            st.info("Hindi audio summary would be available here.")
-                    except Exception as e:
-                        st.warning(f"Error playing audio: {str(e)}. You can still read the Hindi text below.")
-                
-                # Display the Hindi text with better formatting
-                with st.expander("Show Hindi Text"):
-                    hindi_text = response.get("Hindi Summary", "Hindi text not available.")
-                    
-                    # Format the text for better readability
-                    paragraphs = hindi_text.split("। ")
-                    
-                    for paragraph in paragraphs:
-                        if paragraph.strip():
-                            # Add a period if it doesn't end with one
-                            if not paragraph.strip().endswith("।"):
-                                paragraph += "।"
-                            st.markdown(f"<p style='font-size: 16px; margin-bottom: 10px;'>{paragraph}</p>", unsafe_allow_html=True)
-                
-                st.markdown("</div>", unsafe_allow_html=True)
-                
-                st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
-            
-            # Display articles
-            st.markdown("<h3 class='sub-header'>News Articles</h3>", unsafe_allow_html=True)
-            articles = response.get("Articles", [])
-            
-            if not articles:
-                st.info("No articles found for this company.")
-            else:
-                for i, article in enumerate(articles):
-                    display_article(article, i)
-            
-            st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
-            
-            # Display comparative analysis
-            st.markdown("<h3 class='sub-header'>Comparative Analysis</h3>", unsafe_allow_html=True)
-            
-            # Display topic overlap
-            topic_data = response["Comparative Sentiment Score"]["Topic Overlap"]
-            
-            col1, col2 = st.columns(2)
-            
-            with col1:
-                st.markdown("<div class='card'>", unsafe_allow_html=True)
-                st.markdown("<h4>Common Topics</h4>", unsafe_allow_html=True)
-                
-                common_topics = topic_data.get("Common Topics Across All", [])
-                if common_topics:
-                    for topic in common_topics:
-                        st.markdown(f"<span class='topic-tag'>{topic}</span>", unsafe_allow_html=True)
-                else:
-                    st.info("No common topics found across articles.")
-                
-                st.markdown("</div>", unsafe_allow_html=True)
-            
-            with col2:
-                st.markdown("<div class='card'>", unsafe_allow_html=True)
-                st.markdown("<h4>Coverage Comparison</h4>", unsafe_allow_html=True)
-                
-                comparisons = response["Comparative Sentiment Score"].get("Coverage Differences", [])
-                if comparisons:
-                    for i, comparison in enumerate(comparisons[:3]):  # Show only top 3 comparisons
-                        st.markdown(f"<p><strong>{i+1}.</strong> {comparison.get('Comparison', '')}</p>", unsafe_allow_html=True)
-                        st.markdown(f"<p class='info-text'>{comparison.get('Impact', '')}</p>", unsafe_allow_html=True)
-                else:
-                    st.info("No comparative insights available.")
-                
-                st.markdown("</div>", unsafe_allow_html=True)
-            
-            # Display full comparison in expander
-            with st.expander("View All Comparisons"):
-                comparisons = response["Comparative Sentiment Score"].get("Coverage Differences", [])
-                for i, comparison in enumerate(comparisons):
-                    st.markdown(f"<p><strong>{i+1}.</strong> {comparison.get('Comparison', '')}</p>", unsafe_allow_html=True)
-                    st.markdown(f"<p class='info-text'>{comparison.get('Impact', '')}</p>", unsafe_allow_html=True)
-                    st.markdown("<hr>", unsafe_allow_html=True)
-            
-            # Show JSON in example format if requested
-            if show_json:
-                st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
-                st.markdown("<h3 class='sub-header'>Example JSON Format</h3>", unsafe_allow_html=True)
-                
-                # Get the formatted JSON
-                json_output = generate_example_output(company_name)
-                
-                # Display the JSON in a code block
-                st.code(json_output, language="json")
-else:
-    # Display placeholder
-    st.markdown("<div class='card'>", unsafe_allow_html=True)
-    st.markdown("<h3 class='sub-header'>Enter a Company Name to Begin Analysis</h3>", unsafe_allow_html=True)
-    st.markdown("""
-        <p class='info-text'>
-        This application will:
-        </p>
-        <ul class='info-text'>
-            <li>Extract news articles from multiple sources</li>
-            <li>Analyze sentiment (positive, negative, neutral)</li>
-            <li>Identify key topics in each article</li>
-            <li>Perform comparative analysis across articles</li>
-            <li>Generate Hindi speech output summarizing the findings</li>
-        </ul>
-    """, unsafe_allow_html=True)
-    st.markdown("</div>", unsafe_allow_html=True)
-    
-    # Sample output image
-    st.image("https://miro.medium.com/max/1400/1*Ger-949PgQnaje2oa9XMdw.png", caption="Sample sentiment analysis visualization")
-
-# Footer
-st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
-st.markdown("<p class='info-text' style='text-align: center;'>News Summarization & Text-to-Speech Application | Developed with Streamlit and FastAPI</p>", unsafe_allow_html=True) 
+import streamlit as st
+import requests
+import pandas as pd
+import json
+import os
+import matplotlib.pyplot as plt
+import seaborn as sns
+import base64
+from io import BytesIO
+from PIL import Image, ImageEnhance
+import time
+from typing import Dict, Any, List, Optional
+import uuid
+import asyncio
+from pydantic import BaseModel
+import traceback
+
+# Import backend utility functions (assuming these are in a separate utils.py file)
+# For Hugging Face Spaces, you'll need to include these functions in the same file or a utils.py alongside app.py
+from utils import (search_news, analyze_article_sentiment, perform_comparative_analysis, 
+                  translate_to_hindi, text_to_speech, prepare_final_report, NewsArticle)
+
+# For this example, I'll assume utils.py is available. If not, you'd need to paste those function definitions here.
+
+# API Base URL - Not needed since we're integrating directly, but kept for reference
+API_BASE_URL = "http://localhost:8000"
+
+# Define request/response models (from api.py)
+class CompanyRequest(BaseModel):
+    company_name: str
+
+class TextToSpeechRequest(BaseModel):
+    text: str
+    output_filename: Optional[str] = None
+
+class SentimentAnalysisRequest(BaseModel):
+    articles: List[Dict[str, Any]]
+
+# Backend functions adapted from api.py
+async def get_news(company_name: str) -> Dict[str, Any]:
+    try:
+        articles = search_news(company_name, num_articles=5)
+        if not articles:
+            return {"error": f"No news articles found for {company_name}"}
+        article_data = [article.to_dict() for article in articles]
+        return {"articles": article_data}
+    except Exception as e:
+        return {"error": str(e)}
+
+async def analyze_sentiment(articles: List[Dict[str, Any]]) -> Dict[str, Any]:
+    try:
+        news_articles = []
+        for article_dict in articles:
+            article = NewsArticle(
+                title=article_dict["title"],
+                url=article_dict["url"],
+                content=article_dict["content"],
+                summary=article_dict.get("summary", ""),
+                source=article_dict.get("source", ""),
+                date=article_dict.get("date", ""),
+                sentiment=article_dict.get("sentiment", ""),
+                topics=article_dict.get("topics", [])
+            )
+            news_articles.append(article)
+        
+        detailed_sentiment = [analyze_article_sentiment(article) for article in news_articles]
+        comparative_analysis = perform_comparative_analysis(news_articles)
+        return {
+            "sentiment_analysis": {
+                "detailed_sentiment": detailed_sentiment,
+                "comparative_analysis": comparative_analysis
+            }
+        }
+    except Exception as e:
+        return {"error": str(e)}
+
+async def generate_speech(text: str, output_filename: str = None) -> Dict[str, Any]:
+    try:
+        if not output_filename:
+            unique_id = uuid.uuid4().hex
+            output_filename = f"audio_files/{unique_id}.mp3"
+        elif not output_filename.startswith("audio_files/"):
+            output_filename = f"audio_files/{output_filename}"
+        
+        os.makedirs("audio_files", exist_ok=True)
+        hindi_text = translate_to_hindi(text)
+        audio_file = text_to_speech(hindi_text, output_filename)
+        if not audio_file:
+            return {"error": "Failed to generate audio file"}
+        return {"audio_file": audio_file, "text": hindi_text}
+    except Exception as e:
+        return {"error": str(e)}
+
+async def complete_analysis(company_name: str) -> Dict[str, Any]:
+    try:
+        articles = search_news(company_name, num_articles=5)
+        if not articles:
+            return {"error": f"No news articles found for {company_name}"}
+        
+        comparative_analysis = perform_comparative_analysis(articles)
+        final_report = prepare_final_report(company_name, articles, comparative_analysis)
+        
+        unique_id = uuid.uuid4().hex
+        output_filename = f"audio_files/{unique_id}.mp3"
+        hindi_text = final_report["Hindi Summary"]
+        audio_file = text_to_speech(hindi_text, output_filename)
+        
+        formatted_response = {
+            "Company": company_name,
+            "Articles": final_report["Articles"],
+            "Comparative Sentiment Score": {
+                "Sentiment Distribution": comparative_analysis["Sentiment Distribution"],
+                "Coverage Differences": comparative_analysis["Coverage Differences"],
+                "Topic Overlap": {
+                    "Common Topics": comparative_analysis["Topic Overlap"]["Common Topics Across All"],
+                }
+            },
+            "Final Sentiment Analysis": comparative_analysis["Final Sentiment Analysis"],
+            "Hindi Summary": final_report["Hindi Summary"]
+        }
+        
+        unique_topics = comparative_analysis["Topic Overlap"]["Unique Topics By Article"]
+        for article_idx, topics in unique_topics.items():
+            article_num = int(article_idx) + 1
+            formatted_response["Comparative Sentiment Score"]["Topic Overlap"][f"Unique Topics in Article {article_num}"] = topics
+        
+        if len(articles) <= 1:
+            formatted_response["Comparative Sentiment Score"]["Coverage Differences"] = [
+                {
+                    "Comparison": f"Only one article about {company_name} was found, limiting comparative analysis.",
+                    "Impact": "Unable to compare coverage across multiple sources for more comprehensive insights."
+                }
+            ]
+        
+        if audio_file:
+            formatted_response["Audio"] = "[Play Hindi Speech]"
+            formatted_response["_audio_file_path"] = audio_file
+        else:
+            formatted_response["Audio"] = "Failed to generate audio"
+        
+        return formatted_response
+    except Exception as e:
+        error_message = f"Error processing request: {str(e)}"
+        user_message = "An error occurred during analysis. "
+        if "timeout" in str(e).lower():
+            user_message += "There was a timeout when connecting to news sources. Please try again."
+        elif "connection" in str(e).lower():
+            user_message += "There was a connection issue. Please check your internet."
+        elif "not found" in str(e).lower():
+            user_message += f"No information could be found for {company_name}."
+        else:
+            user_message += "Please try again."
+        return {"error": user_message}
+
+# Streamlit UI functions (from app.py)
+def generate_example_output(company_name: str) -> str:
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    result = loop.run_until_complete(complete_analysis(company_name))
+    formatted_output = {
+        "Company": result.get("Company", company_name),
+        "Articles": result.get("Articles", []),
+        "Comparative Sentiment Score": result.get("Comparative Sentiment Score", {
+            "Sentiment Distribution": {},
+            "Coverage Differences": [],
+            "Topic Overlap": {}
+        }),
+        "Final Sentiment Analysis": result.get("Final Sentiment Analysis", ""),
+        "Audio": result.get("Audio", "No audio available")
+    }
+    return json.dumps(formatted_output, indent=2)
+
+def get_sentiment_color(sentiment: str) -> str:
+    if sentiment == "Positive":
+        return "positive"
+    elif sentiment == "Negative":
+        return "negative"
+    else:
+        return "neutral"
+
+def plot_sentiment_distribution(sentiment_data: Dict[str, int]):
+    labels = ["Positive", "Neutral", "Negative"]
+    values = [sentiment_data.get(label, 0) for label in labels]
+    colors = ["#059669", "#6B7280", "#DC2626"]
+    
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.bar(labels, values, color=colors)
+    ax.set_title("Sentiment Distribution", fontsize=16, fontweight='bold')
+    ax.set_ylabel("Number of Articles", fontsize=12)
+    ax.grid(axis='y', linestyle='--', alpha=0.7)
+    for i, v in enumerate(values):
+        ax.text(i, v + 0.1, str(v), ha='center', fontweight='bold')
+    return fig
+
+def display_article(article: Dict[str, Any], index: int):
+    st.markdown(f"<div class='card'>", unsafe_allow_html=True)
+    sentiment = article.get("Sentiment", "Neutral")
+    sentiment_class = get_sentiment_color(sentiment)
+    st.markdown(f"<h3 class='article-title'>{index+1}. {article['Title']}</h3>", unsafe_allow_html=True)
+    st.markdown(f"<span class='{sentiment_class}'>{sentiment}</span>", unsafe_allow_html=True)
+    st.markdown("<div class='article-summary'>", unsafe_allow_html=True)
+    st.markdown(f"{article.get('Summary', 'No summary available.')}", unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True)
+    if "Topics" in article and article["Topics"]:
+        st.markdown("<div>", unsafe_allow_html=True)
+        for topic in article["Topics"]:
+            st.markdown(f"<span class='topic-tag'>{topic}</span>", unsafe_allow_html=True)
+        st.markdown("</div>", unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True)
+
+# Streamlit App
+st.set_page_config(
+    page_title="News Summarization & TTS",
+    page_icon="📰",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+st.markdown("""
+<style>
+    .main-header { font-size: 2.5rem; font-weight: 700; color: #1E3A8A; margin-bottom: 1rem; }
+    .sub-header { font-size: 1.5rem; font-weight: 600; color: #2563EB; margin-top: 1rem; margin-bottom: 0.5rem; }
+    .card { padding: 1.5rem; border-radius: 0.5rem; background-color: #F8FAFC; border: 1px solid #E2E8F0; margin-bottom: 1rem; }
+    .positive { color: #059669; font-weight: 600; }
+    .negative { color: #DC2626; font-weight: 600; }
+    .neutral { color: #6B7280; font-weight: 600; }
+    .topic-tag { display: inline-block; padding: 0.25rem 0.5rem; border-radius: 2rem; background-color: #E5E7EB; color: #1F2937; font-size: 0.75rem; margin-right: 0.5rem; margin-bottom: 0.5rem; }
+    .audio-container { width: 100%; padding: 1rem; background-color: #F3F4F6; border-radius: 0.5rem; margin-top: 1rem; }
+    .info-text { font-size: 0.9rem; color: #4B5563; }
+    .article-title { font-size: 1.2rem; font-weight: 600; color: #111827; margin-bottom: 0.5rem; margin-top: 0.5rem; }
+    .article-summary { font-size: 0.9rem; color: #374151; margin-bottom: 0.5rem; }
+    .section-divider { height: 1px; background-color: #E5E7EB; margin: 1.5rem 0; }
+</style>
+""", unsafe_allow_html=True)
+
+st.markdown("<h1 class='main-header'>📰 News Summarization & Text-to-Speech</h1>", unsafe_allow_html=True)
+st.markdown("""
+<p class='info-text'>
+This application extracts news articles about a company, performs sentiment analysis, conducts comparative analysis,
+and generates a text-to-speech output in Hindi. Enter a company name to get started.
+</p>
+""", unsafe_allow_html=True)
+
+# Sidebar
+st.sidebar.image("https://cdn-icons-png.flaticon.com/512/2593/2593073.png", width=100)
+st.sidebar.title("News Analysis Settings")
+
+company_input_method = st.sidebar.radio(
+    "Select company input method:",
+    options=["Text Input", "Choose from List"]
+)
+
+if company_input_method == "Text Input":
+    company_name = st.sidebar.text_input("Enter Company Name:", placeholder="e.g., Tesla")
+else:
+    companies = ["Apple", "Google", "Microsoft", "Amazon", "Tesla", "Meta", "Netflix", "Uber", "Airbnb", "Twitter"]
+    company_name = st.sidebar.selectbox("Select Company:", companies)
+
+max_articles = st.sidebar.slider("Maximum Articles to Analyze:", min_value=5, max_value=20, value=10)
+analyze_button = st.sidebar.button("Analyze Company News", type="primary")
+audio_speed = st.sidebar.select_slider("TTS Speech Speed:", options=["Slow", "Normal", "Fast"], value="Normal")
+show_json = st.sidebar.checkbox("Show JSON output in example format")
+
+with st.sidebar.expander("About This App"):
+    st.markdown("""
+    This application performs:
+    - News extraction from multiple sources
+    - Sentiment analysis of the content
+    - Topic identification and comparative analysis
+    - Text-to-speech conversion to Hindi
+    """)
+
+# Main content
+if analyze_button and company_name:
+    with st.spinner(f"Analyzing news for {company_name}... This may take a minute"):
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        response = loop.run_until_complete(complete_analysis(company_name))
+        
+        if "error" in response:
+            st.error(response["error"])
+        else:
+            st.markdown(f"<h2 class='sub-header'>Analysis Results for {response['Company']}</h2>", unsafe_allow_html=True)
+            
+            col1, col2 = st.columns([2, 1])
+            with col1:
+                st.markdown("<div class='card'>", unsafe_allow_html=True)
+                st.markdown("<h3 class='sub-header'>Sentiment Overview</h3>", unsafe_allow_html=True)
+                st.markdown(f"{response['Final Sentiment Analysis']}")
+                st.markdown("</div>", unsafe_allow_html=True)
+            with col2:
+                sentiment_data = response["Comparative Sentiment Score"]["Sentiment Distribution"]
+                fig = plot_sentiment_distribution(sentiment_data)
+                st.pyplot(fig)
+            
+            st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
+            
+            if "Audio" in response and response["Audio"] == "[Play Hindi Speech]":
+                st.markdown("<h3 class='sub-header'>Hindi Audio Summary</h3>", unsafe_allow_html=True)
+                audio_file_path = response.get("_audio_file_path")
+                if audio_file_path and os.path.exists(audio_file_path):
+                    st.markdown("<div class='audio-container'>", unsafe_allow_html=True)
+                    st.audio(audio_file_path, format="audio/mp3")
+                    with open(audio_file_path, "rb") as f:
+                        audio_bytes = f.read()
+                        b64 = base64.b64encode(audio_bytes).decode()
+                        href = f'<a href="data:audio/mp3;base64,{b64}" download="hindi_summary.mp3">Download Hindi Audio</a>'
+                        st.markdown(href, unsafe_allow_html=True)
+                    st.markdown("</div>", unsafe_allow_html=True)
+                else:
+                    st.warning("Hindi audio could not be generated.")
+                
+                with st.expander("Show Hindi Text"):
+                    hindi_text = response.get("Hindi Summary", "Hindi text not available.")
+                    paragraphs = hindi_text.split("। ")
+                    for paragraph in paragraphs:
+                        if paragraph.strip():
+                            if not paragraph.strip().endswith("।"):
+                                paragraph += "।"
+                            st.markdown(f"<p style='font-size: 16px; margin-bottom: 10px;'>{paragraph}</p>", unsafe_allow_html=True)
+            
+            st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
+            
+            st.markdown("<h3 class='sub-header'>News Articles</h3>", unsafe_allow_html=True)
+            articles = response.get("Articles", [])
+            if not articles:
+                st.info("No articles found for this company.")
+            else:
+                for i, article in enumerate(articles):
+                    display_article(article, i)
+            
+            st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
+            
+            st.markdown("<h3 class='sub-header'>Comparative Analysis</h3>", unsafe_allow_html=True)
+            col1, col2 = st.columns(2)
+            with col1:
+                st.markdown("<div class='card'>", unsafe_allow_html=True)
+                st.markdown("<h4>Common Topics</h4>", unsafe_allow_html=True)
+                common_topics = response["Comparative Sentiment Score"]["Topic Overlap"].get("Common Topics", [])
+                if common_topics:
+                    for topic in common_topics:
+                        st.markdown(f"<span class='topic-tag'>{topic}</span>", unsafe_allow_html=True)
+                else:
+                    st.info("No common topics found across articles.")
+                st.markdown("</div>", unsafe_allow_html=True)
+            with col2:
+                st.markdown("<div class='card'>", unsafe_allow_html=True)
+                st.markdown("<h4>Coverage Comparison</h4>", unsafe_allow_html=True)
+                comparisons = response["Comparative Sentiment Score"].get("Coverage Differences", [])
+                if comparisons:
+                    for i, comparison in enumerate(comparisons[:3]):
+                        st.markdown(f"<p><strong>{i+1}.</strong> {comparison.get('Comparison', '')}</p>", unsafe_allow_html=True)
+                        st.markdown(f"<p class='info-text'>{comparison.get('Impact', '')}</p>", unsafe_allow_html=True)
+                else:
+                    st.info("No comparative insights available.")
+                st.markdown("</div>", unsafe_allow_html=True)
+            
+            with st.expander("View All Comparisons"):
+                for i, comparison in enumerate(comparisons):
+                    st.markdown(f"<p><strong>{i+1}.</strong> {comparison.get('Comparison', '')}</p>", unsafe_allow_html=True)
+                    st.markdown(f"<p class='info-text'>{comparison.get('Impact', '')}</p>", unsafe_allow_html=True)
+                    st.markdown("<hr>", unsafe_allow_html=True)
+            
+            if show_json:
+                st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
+                st.markdown("<h3 class='sub-header'>Example JSON Format</h3>", unsafe_allow_html=True)
+                json_output = generate_example_output(company_name)
+                st.code(json_output, language="json")
+else:
+    st.markdown("<div class='card'>", unsafe_allow_html=True)
+    st.markdown("<h3 class='sub-header'>Enter a Company Name to Begin Analysis</h3>", unsafe_allow_html=True)
+    st.markdown("""
+        <p class='info-text'>
+        This application will:
+        </p>
+        <ul class='info-text'>
+            <li>Extract news articles from multiple sources</li>
+            <li>Analyze sentiment (positive, negative, neutral)</li>
+            <li>Identify key topics in each article</li>
+            <li>Perform comparative analysis across articles</li>
+            <li>Generate Hindi speech output summarizing the findings</li>
+        </ul>
+    """, unsafe_allow_html=True)
+    st.markdown("</div>", unsafe_allow_html=True)
+    st.image("https://miro.medium.com/max/1400/1*Ger-949PgQnaje2oa9XMdw.png", caption="Sample sentiment analysis visualization")
+
+st.markdown("<div class='section-divider'></div>", unsafe_allow_html=True)
+st.markdown("<p class='info-text' style='text-align: center;'>News Summarization & Text-to-Speech Application | Developed with Streamlit</p>", unsafe_allow_html=True)