Update model.py

model.py

@@ -12,29 +12,26 @@ from nltk.tokenize import sent_tokenize
 from transformers import pipeline
 import numpy as np
 
-# === Load Hugging Face Pipelines ===
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+# === Pipelines ===
+summarizer = pipeline("summarization", model="csebuetnlp/mT5_multilingual_XLSum")
 qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
 emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=1)
 
-# === Summarization Functions ===
+# === Summarization (Brief) ===
 def summarize_review(text, max_len=60, min_len=10):
     return summarizer(text, max_length=max_len, min_length=min_len, do_sample=False)[0]["summary_text"]
 
+# === Smart Summarization ===
 def smart_summarize(text, n_clusters=1):
     sentences = sent_tokenize(text)
     if len(sentences) <= 1:
         return text
-
     tfidf = TfidfVectorizer(stop_words="english")
     tfidf_matrix = tfidf.fit_transform(sentences)
-
     if len(sentences) <= n_clusters:
         return " ".join(sentences)
-
     kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(tfidf_matrix)
     summary_sentences = []
-
     for i in range(n_clusters):
         idx = np.where(kmeans.labels_ == i)[0]
         if not len(idx):
@@ -43,7 +40,6 @@ def smart_summarize(text, n_clusters=1):
         sim = cosine_similarity(avg_vector, tfidf_matrix[idx].toarray())
         most_representative = sentences[idx[np.argmax(sim)]]
         summary_sentences.append(most_representative)
-
     return " ".join(sorted(summary_sentences, key=sentences.index))
 
 # === Emotion Detection ===
@@ -76,6 +72,15 @@ def answer_followup(text, question, verbosity="brief"):
     except Exception:
         return "Sorry, I couldn't generate a follow-up answer."
 
+# === Fast follow-up (no formatting, for /followup) ===
+def answer_only(text, question):
+    try:
+        if not question:
+            return "No question provided."
+        return qa_pipeline({"question": question, "context": text}).get("answer", "No answer found.")
+    except Exception:
+        return "Q&A failed."
+
 # === Optional Explanation Generator ===
 def generate_explanation(text):
     try: