Update app.py

app.py

@@ -53,36 +53,29 @@ def generate(prompt, history, temperature=0.1, max_new_tokens=2048, top_p=0.8, r
     return output
 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".
+    fixed_prompt ="""You are a smart mood analyzer who determines user mood. Based on the user input, classify the mood of the user into one of the four moods: Happy, Sad, Instrumental, or Party. If you find it difficult to classify into one of these four moods, keep the conversation going until you can classify the user's mood. Return a single-word reply from one of the options if you have classified. For example, if you classify a sentence as happy, just respond with "Happy".
 
-    Note: Do not write anything else other than the classified mood if classified.
+Rules:
+1. Do not write anything else other than the classified mood if classified.
+2. If any question or user text cannot be classified, follow up with a question to know the user's mood until you classify the mood.
+3. Mood should be classified only from these 4 classes: Happy, Sad, Instrumental, Party. If not any of these 4, continue with a follow-up question until you classify the mood.
+4. If the user asks something like "I need a coffee", do not classify the mood directly. Ask more follow-up questions to determine their mood."""
 
-    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.
+        conversation = [
+            {"role": "system", "content": system_prompt}
+        ]
 
-    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.
+        for user_prompt, bot_response in history:
+            conversation.extend([
+                {"role": "user", "content": user_prompt},
+                {"role": "assistant", "content": bot_response}
+            ])
 
-    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.
+        conversation.append({"role": "user", "content": message})
 
-    [Examples omitted for brevity]
-    """
-    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:"
-    return prompt
-
-async def text_to_speech(text):
-    communicate = edge_tts.Communicate(text)
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
-        tmp_path = tmp_file.name
-        await communicate.save(tmp_path)
-    return tmp_path
-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:"
-return prompt
+        prompt = self.tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
+        
+        return prompt
 
 async def text_to_speech(text):
     communicate = edge_tts.Communicate(text)