Update app.py

app.py

@@ -1,90 +1,90 @@
-import nltk
-nltk.download('punkt_tab')
-nltk.download('averaged_perceptron_tagger_eng')
-import streamlit as st
-from streamlit_lottie import st_lottie
-import requests
-import time
-from utils import (
-    get_bing_news_articles,
-    analyze_sentiment,
-    extract_topics,
-    comparative_analysis,
-    convert_text_to_hindi_tts,
-)
-from collections import Counter
-# Load Lottie Animation
-def load_lottie_url(url):
-    r = requests.get(url)
-    if r.status_code != 200:
-        return None
-    return r.json()
-
-lottie_animation = load_lottie_url("https://lottie.host/d02e4bd8-cd9c-401e-b143-17fc0ad924a8/o2dLZzU9oO.json")
-
-# UI Layout
-st_lottie(lottie_animation, height=200)
-st.markdown("<h1 style='text-align: center; color: #4CAF50;'>Sentiment Analysis Dashboard</h1>", unsafe_allow_html=True)
-
-
-st.title("News Summarization & Sentiment Analysis with Hindi TTS")
-st.write("Enter a company name to fetch news articles, analyze sentiment, and generate a Hindi summary.")
-
-company = st.text_input("Company Name", "Enter Any Company Name")
-
-if st.button("Generate Report"):
-    with st.spinner("Fetching news articles..."):
-        articles = get_bing_news_articles(company, num_articles=10)
-    
-    if not articles:
-        st.error("No articles found or there was an error fetching the articles.")
-    else:
-        # Process each article: perform sentiment analysis.
-        for article in articles:
-            combined_text = article["title"]
-            if article["summary"]:
-                combined_text += ". " + article["summary"]
-            sentiment, scores = analyze_sentiment(combined_text)
-            article["sentiment"] = sentiment
-            article["sentiment_scores"] = scores
-            # Topics are still extracted but not used in the final summary.
-            article["topics"] = extract_topics(combined_text)
-            time.sleep(0.5)
-        
-        # Display individual article details.
-        st.subheader("Extracted Articles")
-        for idx, article in enumerate(articles, start=1):
-            st.markdown(f"**Article {idx}:**")
-            st.write("Title:", article["title"])
-            st.write("Summary:", article["summary"])
-            st.write("Source:", article["source"])
-            st.write("URL:", article["url"])
-            st.write("Sentiment:", article["sentiment"])
-            st.markdown("---")
-        
-        # Perform comparative analysis for internal metrics (sentiment distribution, coverage insights)
-        analysis = comparative_analysis(articles)
-        st.subheader("Comparative Analysis")
-        st.write("**Sentiment Distribution:**", analysis["Sentiment Distribution"])
-        st.write("**Coverage Differences:**", analysis["Coverage Differences"])
-        
-        # Create a final Hindi summary report that aggregates all the articles.
-        total_articles = len(articles)
-        dist = analysis["Sentiment Distribution"]
-        final_summary = (
-            f"कुल {total_articles} लेखों में से, {dist.get('Positive', 0)} लेख सकारात्मक, "
-            f"{dist.get('Negative', 0)} लेख नकारात्मक, और {dist.get('Neutral', 0)} लेख तटस्थ हैं।\n"
-            "कई लेखों में विक्रय में वृद्धि और आर्थिक विकास पर जोर दिया गया है, जबकि कुछ लेखों में नियामकीय चुनौतियाँ और कानूनी मुद्दों पर चर्चा की गई है।\n"
-            "संपूर्ण रूप से, यह रिपोर्ट दर्शाती है कि कंपनी का समाचार कवरेज मुख्य रूप से सकारात्मक है, "
-            "जो संभावित आर्थिक विकास के संकेत देता है।"
-        )
-        
-        st.subheader("Final Summary Report")
-        st.markdown(final_summary)
-        
-        # Convert the final summary into Hindi speech.
-        with st.spinner("Generating Hindi TTS audio..."):
-            audio_file = convert_text_to_hindi_tts(final_summary, output_file="tesla_summary_hi.mp3")
-        
-        st.success("Audio summary generated!")
-        st.audio(audio_file)
+import nltk
+nltk.download('punkt_tab')
+nltk.download('averaged_perceptron_tagger_eng')
+import streamlit as st
+from streamlit_lottie import st_lottie
+import requests
+import time
+from utils import (
+    get_bing_news_articles,
+    analyze_sentiment,
+    extract_topics,
+    comparative_analysis,
+    convert_text_to_hindi_tts,
+)
+from collections import Counter
+# Load Lottie Animation
+def load_lottie_url(url):
+    r = requests.get(url)
+    if r.status_code != 200:
+        return None
+    return r.json()
+
+lottie_animation = load_lottie_url("https://lottie.host/d02e4bd8-cd9c-401e-b143-17fc0ad924a8/o2dLZzU9oO.json")
+
+# UI Layout
+st_lottie(lottie_animation, height=200)
+st.markdown("<h1 style='text-align: center; color: #4CAF50;'>Sentiment Analysis Dashboard</h1>", unsafe_allow_html=True)
+
+
+st.title("News Summarization & Sentiment Analysis with Hindi TTS")
+st.write("Enter a company name to fetch news articles, analyze sentiment, and generate a Hindi summary.")
+
+company = st.text_input("Company Name")
+
+if st.button("Generate Report"):
+    with st.spinner("Fetching news articles..."):
+        articles = get_bing_news_articles(company, num_articles=10)
+    
+    if not articles:
+        st.error("No articles found or there was an error fetching the articles.")
+    else:
+        # Process each article: perform sentiment analysis.
+        for article in articles:
+            combined_text = article["title"]
+            if article["summary"]:
+                combined_text += ". " + article["summary"]
+            sentiment, scores = analyze_sentiment(combined_text)
+            article["sentiment"] = sentiment
+            article["sentiment_scores"] = scores
+            # Topics are still extracted but not used in the final summary.
+            article["topics"] = extract_topics(combined_text)
+            time.sleep(0.5)
+        
+        # Display individual article details.
+        st.subheader("Extracted Articles")
+        for idx, article in enumerate(articles, start=1):
+            st.markdown(f"**Article {idx}:**")
+            st.write("Title:", article["title"])
+            st.write("Summary:", article["summary"])
+            st.write("Source:", article["source"])
+            st.write("URL:", article["url"])
+            st.write("Sentiment:", article["sentiment"])
+            st.markdown("---")
+        
+        # Perform comparative analysis for internal metrics (sentiment distribution, coverage insights)
+        analysis = comparative_analysis(articles)
+        st.subheader("Comparative Analysis")
+        st.write("**Sentiment Distribution:**", analysis["Sentiment Distribution"])
+        st.write("**Coverage Differences:**", analysis["Coverage Differences"])
+        
+        # Create a final Hindi summary report that aggregates all the articles.
+        total_articles = len(articles)
+        dist = analysis["Sentiment Distribution"]
+        final_summary = (
+            f"कुल {total_articles} लेखों में से, {dist.get('Positive', 0)} लेख सकारात्मक, "
+            f"{dist.get('Negative', 0)} लेख नकारात्मक, और {dist.get('Neutral', 0)} लेख तटस्थ हैं।\n"
+            "कई लेखों में विक्रय में वृद्धि और आर्थिक विकास पर जोर दिया गया है, जबकि कुछ लेखों में नियामकीय चुनौतियाँ और कानूनी मुद्दों पर चर्चा की गई है।\n"
+            "संपूर्ण रूप से, यह रिपोर्ट दर्शाती है कि कंपनी का समाचार कवरेज मुख्य रूप से सकारात्मक है, "
+            "जो संभावित आर्थिक विकास के संकेत देता है।"
+        )
+        
+        st.subheader("Final Summary Report")
+        st.markdown(final_summary)
+        
+        # Convert the final summary into Hindi speech.
+        with st.spinner("Generating Hindi TTS audio..."):
+            audio_file = convert_text_to_hindi_tts(final_summary, output_file="tesla_summary_hi.mp3")
+        
+        st.success("Audio summary generated!")
+        st.audio(audio_file)