Update tts.py

tts.py

@@ -1,93 +1,14 @@
-import yake
-from textblob import TextBlob
-from sentence_transformers import SentenceTransformer, util
-from services.news_fetcher import fetch_news
-from tts import generate_tts
+from gtts import gTTS
+from deep_translator import GoogleTranslator
+import os
 
-# Initialize SBERT & YAKE
-sbert_model = SentenceTransformer("all-MiniLM-L6-v2")
-yake_extractor = yake.KeywordExtractor(lan="en", n=2, dedupLim=0.9, top=5)
+def generate_tts(text, lang="hi"):
+    """Translate text to Hindi and generate TTS audio."""
+    translator = GoogleTranslator(source="auto", target="hi")  # Translate to Hindi
+    translated_text = translator.translate(text)
 
-def analyze_sentiment(text):
-    """Perform sentiment analysis using TextBlob."""
-    if not text or not isinstance(text, str) or not text.strip():
-        return "Neutral"
+    output_path = "static/output.mp3"
+    tts = gTTS(translated_text, lang=lang)
+    tts.save(output_path)
 
-    blob = TextBlob(text)
-    polarity = blob.sentiment.polarity
-    return "Positive" if polarity > 0 else "Negative" if polarity < 0 else "Neutral"
-
-def extract_topics(text):
-    """Extract key topics using YAKE."""
-    if not text or not isinstance(text, str) or not text.strip():
-        return []
-    
-    keywords = yake_extractor.extract_keywords(text)
-    return [kw[0] for kw in keywords]
-
-def extract_common_and_unique_topics(articles):
-    """Identify common topics appearing in multiple articles and extract unique topics."""
-    topic_sets = {f"Article {i+1}": set(extract_topics(article["Summary"])) for i, article in enumerate(articles)}
-
-    all_topics = [topic for topics in topic_sets.values() for topic in topics]
-    common_topics = {topic for topic in all_topics if all_topics.count(topic) > 1}
-
-    unique_topics = {
-        f"Unique Topics in Article {i+1}": list(topics - common_topics) for i, topics in enumerate(topic_sets.values())
-    }
-
-    return {"Common Topics": list(common_topics), **unique_topics}
-
-def compare_articles(articles):
-    """Generate coverage differences by comparing specific article pairs."""
-    comparisons = []
-    pairs = [(0,1), (2,3), (4,5), (6,7), (8,9)]
-    
-    summaries = [article["Summary"] for article in articles]
-    embeddings = sbert_model.encode(summaries, convert_to_tensor=True)
-
-    for i, j in pairs:
-        if i < len(articles) and j < len(articles):
-            similarity = util.pytorch_cos_sim(embeddings[i], embeddings[j]).item()
-
-            comparison_text = f"Article {i+1} discusses '{articles[i]['Title']}', while Article {j+1} focuses on '{articles[j]['Title']}'."
-            impact_text = (
-                f"The first article has a {articles[i]['Sentiment']} sentiment, whereas the second has a {articles[j]['Sentiment']} sentiment. "
-                f"This contrast may influence public perception differently."
-            )
-            comparisons.append({"Comparison": comparison_text, "Impact": impact_text})
-
-    return comparisons
-
-def process_articles(company):
-    """Processes articles to extract sentiment, topics, and comparisons."""
-    articles = fetch_news(company)
-    structured_data = {"Company": company, "Articles": []}
-    sentiments = []
-
-    for i, article in enumerate(articles):
-        summary = article.get("Summary", "No summary available")
-        sentiment = analyze_sentiment(summary)
-        topics = extract_topics(summary)
-        sentiments.append(sentiment)
-
-        structured_data["Articles"].append({
-            "Title": article["Title"],
-            "Summary": summary,
-            "Sentiment": sentiment,
-            "Topics": topics
-        })
-
-    structured_data["Comparative Sentiment Score"] = {
-        "Sentiment Distribution": {s: sentiments.count(s) for s in ["Positive", "Negative", "Neutral"]},
-        "Coverage Differences": compare_articles(structured_data["Articles"]),
-        "Topic Overlap": extract_common_and_unique_topics(structured_data["Articles"])
-    }
-
-    max_sentiment = max(structured_data["Comparative Sentiment Score"]["Sentiment Distribution"], 
-                        key=structured_data["Comparative Sentiment Score"]["Sentiment Distribution"].get)
-    structured_data["Final Sentiment Analysis"] = f"{company} latest news coverage is mostly {max_sentiment}."
-
-    structured_data["Audio"] = generate_tts(structured_data["Final Sentiment Analysis"])
-
-    return structured_data
+    return output_path  # Return file path for playback