approach_library/main.py

# import json
# import time
# from news_extraction import extract_news
# from news_summarisation import summarize_text
# from news_sentiment import analyze_sentiment
# from topic_extraction import preprocess_text, train_lda, extract_topic_words
# from comparative_analysis import comparative_sentiment_analysis
# from text_to_speech import text_to_speech  # ✅ Import the TTS function

# def main():
#     # User input for the company/topic
#     company = input("Enter the company name for analysis: ").strip()

#     # Extract news articles
#     start_time = time.time()
#     articles = extract_news(company)
#     extraction_time = time.time() - start_time

#     if not articles:
#         print("No news articles found. Try a different company.")
#         return

#     articles_data = []  # List to store processed articles

#     # Extract texts from articles for sentiment analysis
#     texts = [article["text"] 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["text"])  # Summarize article
#         summarization_time = time.time() - start_time

#         # Extract topics for the specific article
#         preprocessed_text = preprocess_text([article["text"]])
#         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 Hindi TTS

#     # ✅ Construct final JSON output
#     output = {
#         "Company": company,
#         "Articles": articles_data,
#         "Comparative Sentiment Score": analysis_result,
#         "Final Sentiment Analysis": final_summary,
#         "Audio": f"[Play {audio_file}]"  # ✅ Include a playable reference
#     }

#     # Print JSON output
#     print(json.dumps(output, indent=4, ensure_ascii=False))

#     # Save JSON output to file
#     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 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 the TTS function

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["text"] 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["text"])  # Summarize article
        summarization_time = time.time() - start_time

        # Extract topics for the specific article
        preprocessed_text = preprocess_text([article["text"]])
        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))