Create app.py

app.py

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+# Importing Libraries and functions from utils.py in approach_api
+import streamlit as st
+from approach_api.utils.news_extraction_api import extract_news
+from approach_api.utils.news_summarisation import summarize_text
+from approach_api.utils.news_sentiment import analyze_sentiment
+from approach_api.utils.topic_extraction import preprocess_text, train_lda, extract_topic_words
+from approach_api.utils.comparative_analysis import comparative_sentiment_analysis
+from approach_api.utils.text_to_speech import text_to_speech
+import os
+
+# Function
+def analyze_company_news(company):
+    st.write(f"Analyzing company: {company}")
+    
+    with st.spinner("Fetching news articles..."):
+        articles = extract_news(company)
+        if not articles:
+            st.error("No news articles found. Try a different company.")
+            return None
+        st.write(f"Found {len(articles)} articles")
+    
+    articles_data = []
+    texts = [article["content"] for article in articles] 
+    
+    with st.spinner("Performing sentiment analysis..."):
+        sentiment_results = analyze_sentiment(texts)
+        st.write(f"Sentiment analysis completed for {len(sentiment_results['Predicted Sentiment'])} articles")
+    
+    for article, sentiment in zip(articles, sentiment_results["Predicted Sentiment"]):
+        summary = summarize_text(article["content"])
+        preprocessed_text = preprocess_text([article["content"]])
+        lda_model, dictionary = train_lda(preprocessed_text)
+        topic_words = extract_topic_words(lda_model)
+        
+        articles_data.append({
+            "Title": article["title"],
+            "Summary": summary,
+            "Sentiment": sentiment,
+            "Topics": topic_words
+        })
+    
+    with st.spinner("Performing comparative analysis..."):
+        analysis_result = comparative_sentiment_analysis(company, articles_data)
+        st.write("Comparative analysis completed")
+        st.write("Analysis result:", analysis_result)
+    
+    final_summary = f"{company}’s latest news coverage is mostly {analysis_result['Final Sentiment Analysis']}."
+    
+    with st.spinner("Generating Hindi TTS summary..."):
+        try:
+            audio_file = text_to_speech(final_summary)
+            if os.path.exists(audio_file):
+                st.write(f"TTS summary generated: {audio_file}")
+            else:
+                st.error("Failed to generate TTS summary")
+                audio_file = None
+        except Exception as e:
+            st.error(f"TTS generation failed: {str(e)}")
+            audio_file = None
+    
+    return {
+        "Company": company,
+        "Articles": articles_data,
+        "Comparative Sentiment Score": analysis_result,
+        "Audio": audio_file
+    }
+
+st.title("Company News Analysis")
+company = st.text_input("Enter the company name for analysis:")
+if st.button("Analyze") and company:
+    st.write(f"Starting analysis for: {company}")
+    result = analyze_company_news(company)
+    if result:
+        st.subheader(f"Analysis for {result['Company']}")
+        
+        for article in result["Articles"]:
+            st.write(f"**Title:** {article['Title']}")
+            st.write(f"**Summary:** {article['Summary']}")
+            st.write(f"**Sentiment:** {article['Sentiment']}")
+            st.write(f"**Topics:** {', '.join(article['Topics'])}")
+            st.markdown("---")
+        
+        st.subheader("Comparative Sentiment Score")
+        st.json(result["Comparative Sentiment Score"])
+        
+        st.subheader("Hindi TTS Summary")
+        if result["Audio"]:
+            st.audio(result["Audio"], format="audio/mp3")
+        else:
+            st.warning("TTS summary not available")