Update app.py

app.py

@@ -36,31 +36,35 @@ def generate_story(theme):
     return story
 
 def generate_response(user_input):
-    response_prompt = (
-        f"A user is feeling very sad and overwhelmed: '{user_input}'. "
-        "You are a compassionate support bot. Respond with empathy, encouragement, and reassurance."
-    )
-    input_ids = gpt2_tokenizer.encode(response_prompt, return_tensors='pt')
+    # Limit user input length to prevent overflow issues
+    truncated_input = user_input[:200]
+    
+    # Construct a simpler prompt for generating empathetic responses
+    prompt = f"The user is feeling: '{truncated_input}'. Respond with empathy, compassion, and encouragement."
+    
+    # Encode the prompt
+    input_ids = gpt2_tokenizer.encode(prompt, return_tensors='pt')
+    
+    # Generate the response
     response_ids = gpt2_model.generate(
         input_ids,
-        max_length=150,
-        temperature=0.8,
-        top_k=40,
-        repetition_penalty=1.1,
+        max_length=100,
+        temperature=0.7,
+        top_p=0.9,
+        top_k=50,
+        repetition_penalty=1.2,
         num_return_sequences=1
-
     )
     
-    # Decode the response and clean it up by removing the prompt
+    # Decode and clean up the generated response
     response = gpt2_tokenizer.decode(response_ids[0], skip_special_tokens=True)
-    
-    # Strip out the prompt portion to get a clean, empathetic message
-    cleaned_response = response.replace(f"A user is feeling very sad and overwhelmed:{user_input}.You are a compassionate support bot. Respond with empathy, encouragement, and reassurance", "").strip()
+    cleaned_response = response.replace(prompt, "").strip()
     
     return cleaned_response
 
 
 
+
 # Analyze user input for emotional tone
 def get_emotion(user_input):
     emotions = emotion_classifier(user_input)