Update app.py

app.py

@@ -36,8 +36,8 @@ def generate_story(theme):
     return story
 
 def generate_response(user_input):
-    # Empathy-focused prompt to guide the bot
-    response_prompt = f"The user has shared the following: '{user_input}'. Respond with empathy, compassion, and understanding. Acknowledge their sadness and offer comforting, reassuring words. Show that you care and validate their feelings without giving unsolicited advice."
+    # 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."
     
     # Generate the response using the GPT-2 model
     input_ids = gpt2_tokenizer.encode(response_prompt, return_tensors='pt')
@@ -54,12 +54,11 @@ 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"The user has shared the following: '{user_input}'. Respond with empathy, compassion, and understanding. Acknowledge their sadness and offer comforting, reassuring words. Show that you care and validate their feelings without giving unsolicited advice.", "").strip()
+    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()
     
     return cleaned_response
 
 
-
 # Analyze user input for emotional tone
 def get_emotion(user_input):
     emotions = emotion_classifier(user_input)