Update app.py

app.py

@@ -31,14 +31,15 @@ def load_keybert_model():
     return KeyBERT(model="all-MiniLM-L6-v2")
 
 # ─── BlenderBot Response Pipeline ───────────────────────────────────────────
-from transformers import Conversation
-
 @st.cache_resource
 def load_response_pipeline():
-    # High-level conversational helper using BlenderBot 400M Distill
+    # Use BlenderBot 400M Distill for text generation
     return pipeline(
-        "conversational",
-        model="facebook/blenderbot-400M-distill"
+        "text2text-generation",
+        model="facebook/blenderbot-400M-distill",
+        tokenizer="facebook/blenderbot-400M-distill",
+        max_new_tokens=150,
+        do_sample=False
     )
 
 LABEL_MAP = {
@@ -102,17 +103,19 @@ def main():
     # Generate appropriate reply
     response_pipeline = load_response_pipeline()
     if max_label in ["Positive", "Very Positive"]:
-        user_input = f"The customer said: \"{review}\". Write a warm, appreciative two-sentence reply celebrating their positive experience."
+        prompt = (
+            f"You are a friendly customer success representative. The customer said: \"{review}\". "
+            "Write two sentences to express gratitude and highlight their positive experience."
+        )
     else:
-        user_input = (
-            f"The customer said: \"{review}\". Identified issues: {', '.join([kw for kw, _ in keywords])}. "
-            "First, ask 1-2 clarifying questions to better understand their situation. "
+        prompt = (
+            f"You are a helpful customer support specialist. The customer said: \"{review}\". "
+            f"Identified issues: {', '.join([kw for kw, _ in keywords])}. "
+            "First, ask 1-2 clarifying questions to understand their situation. "
             "Then provide two concrete suggestions or next steps to address these issues."
         )
-    conv = Conversation(user_input)
-    response = response_pipeline(conv)
-    # Grab the latest generated response
-    reply = response.generated_responses[-1]
+    result = response_pipeline(prompt)
+    reply = result[0]['generated_text'].strip()
 
     st.subheader("Generated Reply")
     st.write(reply)
@@ -120,4 +123,3 @@ def main():
 
 if __name__ == '__main__':
     main()
-