Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+from utils import fetch_news, analyze_sentiment, summarize_text, generate_hindi_tts
+
+st.title("News Summarization and Sentiment Analysis")
+
+# Input company name
+company_name = st.text_input("Enter Company Name (e.g., Tesla)", "Tesla")
+
+if st.button("Analyze"):
+    # Fetch news
+    articles = fetch_news(company_name)
+    
+    if articles:
+        st.write(f"Found {len(articles)} articles for {company_name}")
+        report = {"Company": company_name, "Articles": [], "Comparative Sentiment Score": {"Sentiment Distribution": {"Positive": 0, "Negative": 0, "Neutral": 0}}}
+
+        # Process each article
+        for article in articles[:10]:  # Limit to 10 articles
+            summary = summarize_text(article['content'])
+            sentiment = analyze_sentiment(article['content'])
+            topics = ["Business"]  # Placeholder
+            
+            report["Articles"].append({
+                "Title": article['title'],
+                "Summary": summary,
+                "Sentiment": sentiment,
+                "Topics": topics
+            })
+            report["Comparative Sentiment Score"]["Sentiment Distribution"][sentiment] += 1
+
+        # Comparative Analysis (simplified)
+        comparative = f"Positive articles focus on growth, while negative ones highlight challenges."
+        report["Comparative Analysis"] = comparative
+
+        # Display report
+        st.json(report)
+
+        # Generate and play Hindi TTS
+        hindi_summary = f"{company_name} की खबरों का सारांश: सकारात्मक {report['Comparative Sentiment Score']['Sentiment Distribution']['Positive']}, नकारात्मक {report['Comparative Sentiment Score']['Sentiment Distribution']['Negative']}।"
+        audio_file = generate_hindi_tts(hindi_summary)
+        st.audio(audio_file, format="audio/wav")
+
+    else:
+        st.error("No articles found!")