Update app.py

app.py

@@ -56,22 +56,32 @@ def generate(prompt, history, temperature=0.1, max_new_tokens=2048, top_p=0.8, r
 def format_prompt(message, history):
     """Formats the prompt including fixed instructions and conversation history."""
     fixed_prompt = """
-    You are a smart mood analyser, who determines user mood. Based on the user input, classify the mood of the user into one of the four moods {Happy, Sad, Instrumental, Party}. If you are finding it difficult to classify into one of these four moods, keep the conversation going on until we classify the user's mood. Return a single-word reply from one of the options if you have classified. Suppose you classify a sentence as happy, then just respond with "happy".
-
-    Note: Do not write anything else other than the classified mood if classified.
-
-    Note: If any question or any user text cannot be classified, follow up with a question to know the user's mood until you classify the mood.
-
-    Note: Mood should be classified only from any of these 4 classes {Happy, Sad, Instrumental, Party}, if not any of these 4 then continue with a follow-up question until you classify the mood.
-
-    Note: if user asks something like i need a coffee then do not classify the mood directly and ask more follow-up questions as asked in examples.
+    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.
     """
     prompt = f"{fixed_prompt}\n"
-    for user_prompt, bot_response in history:
-        prompt += f"User: {user_prompt}\nLLM Response: {bot_response}\n"
-    prompt += f"User: {message}\nLLM Response:"
+    
+    # Add conversation 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 += f"User: {message}\nAssistant:"
     return prompt
 
+
 async def text_to_speech(text):
     communicate = edge_tts.Communicate(text)
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: