Update app.py

app.py

@@ -37,10 +37,10 @@ def generate_story(theme):
 
 
 def generate_response(user_input):
-    # Update the prompt to be more natural and empathetic.
-    response_prompt = f"User shares: '{user_input}'. Respond with empathy, encouragement, and reassurance. Be kind and understanding, offering support for their situation."
+    # Enhanced empathetic prompt
+    response_prompt = f"User shares: '{user_input}'. Respond with empathy and understanding. Acknowledge their feelings and offer words of comfort and encouragement. Your response should focus on offering reassurance and emotional support, not practical advice."
     
-    # Generate the response using the model (this could be GPT-2 or DialoGPT).
+    # Generate the response using the model
     input_ids = gpt2_tokenizer.encode(response_prompt, return_tensors='pt')
     response_ids = gpt2_model.generate(
         input_ids,
@@ -51,15 +51,18 @@ def generate_response(user_input):
         num_return_sequences=1
     )
     
-    # Decode and return the response
+    # Decode and clean up the response
     response = gpt2_tokenizer.decode(response_ids[0], skip_special_tokens=True)
     
-    # Clean up the response to avoid the model repeating the prompt
-    cleaned_response = response.replace(f"You are a compassionate, kind,and empathetic support bot. A user has shared their feelings: '{user_input}'. Respond with empathy, encouragement, and motivation.", "").strip()
+    # Clean response from repeating the prompt
+    cleaned_response = response.replace(f"User shares: '{user_input}'. Respond with empathy and understanding. Acknowledge their feelings and offer words of comfort and encouragement. Your response should focus on offering reassurance and emotional support, not practical advice.", "").strip()
+    
+    # Return the cleaned response
     return cleaned_response
 
 
 
+
 # Analyze user input for emotional tone
 def get_emotion(user_input):
     emotions = emotion_classifier(user_input)