Update app.py

app.py

@@ -36,8 +36,8 @@ def generate_story(theme):
     return story
 
 def generate_response(user_input):
-    # Refined prompt focusing only on empathetic and supportive language
-    response_prompt = f"You are a compassionate and empathetic support bot. A user has shared their feelings: '{user_input}'. Please respond with empathy, validation of their emotions, and offer support without providing unsolicited advice. Be reassuring and comforting, showing understanding of their emotional pain."
+    # Refined prompt with a clear and empathetic tone
+    response_prompt = f"You are a kind and empathetic support bot. A user is sharing their feelings: '{user_input}'. Respond with kindness and empathy, offering emotional validation. Keep the tone soft and comforting, and avoid any philosophical or unrelated explanations. Offer support and let them know that their feelings are valid."
     
     # Generate the response using the GPT-2 model
     input_ids = gpt2_tokenizer.encode(response_prompt, return_tensors='pt')
@@ -54,11 +54,12 @@ def generate_response(user_input):
     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"You are a compassionate and empathetic support bot. A user has shared their feelings: '{user_input}'. Please respond with empathy, validation of their emotions, and offer support without providing unsolicited advice. Be reassuring and comforting, showing understanding of their emotional pain.", "").strip()
+    cleaned_response = response.replace(f"You are a kind and empathetic support bot. A user is sharing their feelings: '{user_input}'. Respond with kindness and empathy, offering emotional validation. Keep the tone soft and comforting, and avoid any philosophical or unrelated explanations. Offer support and let them know that their feelings are valid.", "").strip()
     
     return cleaned_response
 
 
+
 # Analyze user input for emotional tone
 def get_emotion(user_input):
     emotions = emotion_classifier(user_input)