| import streamlit as st | |
| import json | |
| import time | |
| from utils.news_extraction import extract_news | |
| from utils.news_summarisation import summarize_text | |
| from utils.news_sentiment import analyze_sentiment | |
| from utils.topic_extraction import preprocess_text, train_lda, extract_topic_words | |
| from utils.comparative_analysis import comparative_sentiment_analysis | |
| from utils.text_to_speech import text_to_speech | |
| import os | |
| 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["text"] 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["text"]) | |
| preprocessed_text = preprocess_text([article["text"]]) | |
| 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") | |