Update app.py

app.py

@@ -47,28 +47,29 @@ def generate(prompt, history, temperature=0.1, max_new_tokens=2048, top_p=0.8, r
 
     for response in stream:
         output += response.token.text
-        mood, is_classified = classify_mood(output)
-        if is_classified:
-            playlist_message = f"Playing {mood.capitalize()} playlist for you!"
-            return playlist_message
-    return output
+        
+    # Check if the output is a single mood word (confirmed by user)
+    if output.strip().lower() in ["happy", "sad", "instrumental", "party"]:
+        return f"Playing {output.strip().capitalize()} playlist for you!"
+    elif output.strip().lower() == "unclear":
+        return "I'm having trouble determining your mood. Could you tell me more explicitly how you're feeling?"
+    else:
+        return output.strip()
 
 def format_prompt(message, history):
     """Formats the prompt including fixed instructions and conversation history."""
     fixed_prompt = """
-    You are a smart mood analyzer tasked with determining the user's mood for a music recommendation system. Your goal is to classify the user's mood into one of four categories: Happy, Sad, Instrumental, or Party.
-
-    Instructions:
-    1. Engage in a conversation with the user to understand their mood.
-    2. Ask relevant questions to guide the conversation towards mood classification.
-    3. If the user's mood is clear, respond with a single word: "Happy", "Sad", "Instrumental", or "Party".
-    4. If the mood is unclear, continue the conversation with a follow-up question.
-    5. Limit the conversation to a maximum of 5 exchanges.
-    6. Do not classify the mood prematurely if it's not evident from the user's responses.
-    7. Focus on the user's emotional state rather than specific activities or preferences.
-    8. If unable to classify after 5 exchanges, respond with "Unclear" to indicate the need for more information.
-
-    Remember: Your primary goal is mood classification. Stay on topic and guide the conversation towards understanding the user's emotional state.
+    You are a smart mood analyzer for a music recommendation system. Your goal is to determine the user's current mood and suggest an appropriate music playlist. Follow these instructions carefully:
+
+    1. Engage in a conversation to understand the user's mood. Don't assume their mood based on activities or preferences.
+    2. Classify the mood into one of four categories: Happy, Sad, Instrumental, or Party.
+    3. If the mood is unclear, ask relevant follow-up questions. Do not classify prematurely.
+    4. Before suggesting a playlist, always ask for confirmation. For example: "It sounds like you might be in a [mood] mood. Would you like me to play a [mood] playlist for you?"
+    5. Only respond with a single mood word (Happy, Sad, Instrumental, or Party) if the user explicitly confirms they want that type of playlist.
+    6. If you can't determine the mood after 5 exchanges, respond with "Unclear".
+    7. Stay on topic and focus on understanding the user's current emotional state.
+
+    Remember: Your primary goal is accurate mood classification and appropriate music suggestion. Always get confirmation before playing a playlist.
     """
     prompt = f"{fixed_prompt}\n"
     
@@ -76,7 +77,7 @@ def format_prompt(message, history):
     for i, (user_prompt, bot_response) in enumerate(history):
         prompt += f"User: {user_prompt}\nAssistant: {bot_response}\n"
         if i == 3:  # This is the 4th exchange (0-indexed)
-            prompt += "Note: This is the last exchange. Classify the mood if possible or respond with 'Unclear'.\n"
+            prompt += "Note: This is the last exchange. If the mood is still unclear, respond with 'Unclear'.\n"
     
     prompt += f"User: {message}\nAssistant:"
     return prompt