Update app.py

app.py

@@ -20,27 +20,43 @@ llm = Llama(
 
 def smart_response(message, history):
     
-    # --- SMART ROUTER ---
-    triggers = ["should", "opinion", "think", "good", "bad", "pros", "cons", "benefit", "risk", "impact", "why"]
+    # --- 1. EXPANDED TRIGGERS ---
+    # We add common decision words (English & French) to ensure we catch everything.
+    triggers = [
+        # English
+        "should", "opinion", "think", "good", "bad", "pros", "cons", 
+        "benefit", "risk", "impact", "why", "better", "safe", "worth", 
+        "buy", "choose", "prefer", "best",
+        # French
+        "sain", "dangereux", "mieux", "avis", "pensez", "effet", 
+        "faut", "acheter", "choisir", "bien", "mal", "pourquoi"
+    ]
+    
     is_opinion = any(t in message.lower() for t in triggers)
 
+    # --- 2. RESPONSE PRIMING ---
     if is_opinion:
         print(f"🧠 OPINION MODE: {message}")
-        # DUAL FRAME PROMPT
-        # We use the standard instruction format the model learned during fine-tuning.
+        
+        # We start writing the response FOR the model.
+        # By adding "**POSITIVE FRAMING:**" at the end, the model HAS to fill it in.
         prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
 
 ### Instruction:
-{message}
+Analyze the following topic. You must provide the answer in two distinct frames:
+1. POSITIVE FRAMING (Arguments For / Benefits)
+2. NEGATIVE FRAMING (Arguments Against / Risks)
+
+Topic: {message}
 
 ### Response:
-"""
+**POSITIVE FRAMING:**"""
+        
+        # We remove "POSITIVE FRAMING" from the output later so it doesn't print twice
+        prefix_to_add = "**POSITIVE FRAMING:**"
         
     else:
         print(f"ℹ️ CHAT MODE: {message}")
-        # FACTUAL PROMPT
-        # CRITICAL FIX: We use the SAME format (Alpaca), but we add a specific
-        # instruction telling the model to be "concise" and "factual".
         prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
 
 ### Instruction:
@@ -48,11 +64,10 @@ You are a helpful assistant. Answer the following question concisely and directl
 
 ### Response:
 """
+        prefix_to_add = ""
 
-    # --- GENERATION ---
-    # We add "###" to the stop tokens. This cuts off the model immediately 
-    # if it tries to start generating "Solution 1" or a new "### Instruction".
-    stop_tokens = ["</s>", "###", "Solution"]
+    # --- 3. GENERATION ---
+    stop_tokens = ["</s>", "###", "User:", "Instruction:"]
 
     output = llm(
         prompt,
@@ -62,7 +77,11 @@ You are a helpful assistant. Answer the following question concisely and directl
         echo=False
     )
     
-    return output['choices'][0]['text'].strip()
+    text = output['choices'][0]['text'].strip()
+    
+    # If we used priming, we need to attach the prefix back to the start
+    final_response = prefix_to_add + " " + text
+    return final_response
 
 # 3. Launch Interface
 demo = gr.ChatInterface(