Update app.py

app.py

@@ -340,7 +340,7 @@ def semantic_search(query, embeddings, model, threshold=0.5):
     sims = cosine_similarity(query_embedding, embeddings)[0]
     return [(i, s) for i, s in enumerate(sims) if s > threshold]
 
-def rag_summarize(texts, summarizer, top_k=10):  # increased from 5 to 10
+def rag_summarize(texts, summarizer, top_k=10):
     if not texts:
         return "No relevant content to summarize."
     vect = TfidfVectorizer()
@@ -350,7 +350,7 @@ def rag_summarize(texts, summarizer, top_k=10):  # increased from 5 to 10
     top_indices = scores.argsort()[::-1][:top_k]
     ctx = "\n".join(texts[i] for i in top_indices)
     prompt = "summarize: " + ctx[:1024]
-    out = summarizer(prompt, max_length=250, min_length=80, do_sample=False)  
+    out = summarizer(prompt, max_length=200, min_length=80, do_sample=False)  
     return out[0]['summary_text']
 
 def extract_month_year(q):