| # import json | |
| # import time | |
| # from utils.news_extraction_api import fetch_articles | |
| # 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 | |
| # def main(): | |
| # company = input("Enter the company name for analysis: ").strip() | |
| # # Extract news articles | |
| # start_time = time.time() | |
| # articles = fetch_articles(company, num_articles=2) # Fetch 2 articles | |
| # extraction_time = time.time() - start_time | |
| # print(f"✅ Articles extracted in {extraction_time:.2f} seconds") | |
| # if not articles: | |
| # print("⚠️ No news articles found. Try a different company.") | |
| # return | |
| # articles_data = [] | |
| # all_topics = [] # Collect all topics for better analysis | |
| # for article in articles: | |
| # text = article.get("content", "").strip() | |
| # if not text: | |
| # print(f"⚠️ Skipping article '{article.get('title', 'No Title')}' due to missing content.") | |
| # continue | |
| # # Perform sentiment analysis | |
| # start_time = time.time() | |
| # sentiment_result = analyze_sentiment([text]) | |
| # sentiment = sentiment_result.get("Predicted Sentiment", ["Unknown"])[0] | |
| # sentiment_time = time.time() - start_time | |
| # print(f"✅ Sentiment analysis completed in {sentiment_time:.2f} seconds") | |
| # # Summarize the article | |
| # start_time = time.time() | |
| # summary = summarize_text(text) | |
| # summary_time = time.time() - start_time | |
| # print(f"✅ Summary generation completed in {summary_time:.2f} seconds") | |
| # # Extract topics | |
| # start_time = time.time() | |
| # preprocessed_text = preprocess_text([text]) | |
| # if not preprocessed_text: | |
| # print(f"⚠️ No meaningful text extracted for LDA topic modeling in '{article.get('title', 'No Title')}'.") | |
| # topic_words = [] | |
| # else: | |
| # lda_model, dictionary = train_lda(preprocessed_text) | |
| # topic_words = extract_topic_words(lda_model) | |
| # topic_time = time.time() - start_time | |
| # print(f"✅ Topic extraction completed in {topic_time:.2f} seconds") | |
| # # Store processed data | |
| # articles_data.append({ | |
| # "Title": article.get("title", "No Title"), | |
| # "Summary": summary, | |
| # "Sentiment": sentiment, | |
| # "Topics": topic_words if topic_words else [] | |
| # }) | |
| # # Collect topics for comparative analysis | |
| # if topic_words: | |
| # all_topics.extend(topic_words) | |
| # # Ensure articles_data is not empty before analysis | |
| # if not articles_data: | |
| # print("⚠️ No valid articles with content were processed.") | |
| # return | |
| # # Perform comparative sentiment analysis | |
| # start_time = time.time() | |
| # analysis_result = comparative_sentiment_analysis(company, articles_data) | |
| # analysis_time = time.time() - start_time | |
| # print(f"✅ Comparative sentiment analysis completed in {analysis_time:.2f} seconds") | |
| # # Correctly extract "Comparative Sentiment Score" | |
| # comparative_score = analysis_result.get("Comparative Sentiment Score", {}) | |
| # sentiment_distribution = comparative_score.get("Sentiment Distribution", {}) | |
| # coverage_differences = comparative_score.get("Coverage Differences", {}) | |
| # topic_overlap = comparative_score.get("Topic Overlap", []) | |
| # # Debugging check | |
| # if not sentiment_distribution: | |
| # print("⚠️ No sentiment distribution detected.") | |
| # if not coverage_differences: | |
| # print("⚠️ No coverage differences found.") | |
| # if not topic_overlap: | |
| # print("⚠️ No topic overlap detected among articles.") | |
| # # Final sentiment summary | |
| # final_sentiment_analysis = analysis_result.get("Final Sentiment Analysis", "Analysis could not be completed.") | |
| # # Generate summary speech | |
| # start_time = time.time() | |
| # final_summary = f"{company}’s latest news coverage is mostly {final_sentiment_analysis}." | |
| # audio_file = text_to_speech(final_summary) | |
| # audio_time = time.time() - start_time | |
| # print(f"✅ Summary speech generation completed in {audio_time:.2f} seconds") | |
| # # Construct final JSON output | |
| # output = { | |
| # "Company": company, | |
| # "Articles": articles_data, | |
| # "Comparative Sentiment Score": { | |
| # "Sentiment Distribution": sentiment_distribution, | |
| # "Coverage Differences": coverage_differences, | |
| # "Topic Overlap": topic_overlap | |
| # }, | |
| # "Extracted Topics": list(set(all_topics)), # Unique topics across articles | |
| # "Final Sentiment Analysis": final_summary, | |
| # "Audio": f"[Play {audio_file}]" | |
| # } | |
| # # Print JSON output | |
| # print(json.dumps(output, indent=4, ensure_ascii=False)) | |
| # # Save JSON output | |
| # with open(f"{company}_news_analysis.json", "w", encoding="utf-8") as json_file: | |
| # json.dump(output, json_file, indent=4, ensure_ascii=False) | |
| # if __name__ == "__main__": | |
| # main() | |
| import json | |
| import time | |
| from utils.news_extraction_api 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 | |
| def analyze_company_news(company): | |
| # Extract news articles | |
| start_time = time.time() | |
| articles = extract_news(company) | |
| extraction_time = time.time() - start_time | |
| if not articles: | |
| return {"message": "No news articles found. Try a different company."} | |
| articles_data = [] # List to store processed articles | |
| # Extract texts from articles for sentiment analysis | |
| texts = [article["content"] for article in articles] | |
| # Perform sentiment analysis | |
| start_time = time.time() | |
| sentiment_results = analyze_sentiment(texts) | |
| sentiment_time = time.time() - start_time | |
| # Process each article | |
| for i, (article, sentiment) in enumerate(zip(articles, sentiment_results["Predicted Sentiment"]), start=1): | |
| start_time = time.time() | |
| summary = summarize_text(article["content"]) # Summarize article | |
| summarization_time = time.time() - start_time | |
| # Extract topics for the specific article | |
| preprocessed_text = preprocess_text([article["content"]]) | |
| lda_model, dictionary = train_lda(preprocessed_text) | |
| topic_words = extract_topic_words(lda_model) | |
| article_entry = { | |
| "Title": article["title"], | |
| "Summary": summary, | |
| "Sentiment": sentiment, | |
| "Topics": topic_words | |
| } | |
| articles_data.append(article_entry) | |
| # Perform comparative sentiment analysis | |
| analysis_result = comparative_sentiment_analysis(company, articles_data) | |
| # Generate a summary speech for the entire report | |
| final_summary = f"{company}’s latest news coverage is mostly {analysis_result['Final Sentiment Analysis']}." | |
| audio_file = text_to_speech(final_summary) # Generate TTS | |
| # Construct final JSON output | |
| output = { | |
| "Company": company, | |
| "Articles": articles_data, | |
| "Comparative Sentiment Score": analysis_result, | |
| "Audio": f"[Play {audio_file}]" # Include a playable reference | |
| } | |
| return output | |
| if __name__ == "__main__": | |
| company = input("Enter the company name for analysis: ").strip() | |
| result = analyze_company_news(company) | |
| print(json.dumps(result, indent=4, ensure_ascii=False)) |