Update app.py

app.py

@@ -34,23 +34,35 @@ def predict(message, history):
         # Memasukkan history agar chatbot punya ingatan
         messages = [{"role": "system", "content": system_prompt}]
         
-        for entry in history:
-            # Menangani format history Gradio yang bervariasi (list atau dict)
-            if isinstance(entry, dict):
-                messages.append(entry)
-            elif isinstance(entry, (list, tuple)):
-                messages.append({"role": "user", "content": str(entry[0])})
-                messages.append({"role": "assistant", "content": str(entry[1])})
-    
-        messages.append({"role": "user", "content": str(message)})
-    
+        if history:
+            for interaction in history:
+                if isinstance(interaction, dict):
+                    # Format Gradio 4+ (Dictionary)
+                    role = interaction.get("role", "user")
+                    content = interaction.get("content", "")
+                    # Jika content adalah list (multimodal), ambil elemen pertama
+                    if isinstance(content, list): content = str(content[0])
+                    messages.append({"role": role, "content": str(content)})
+                elif isinstance(interaction, (list, tuple)):
+                    # Format Gradio Legacy [user, assistant]
+                    u_msg = interaction[0] if len(interaction) > 0 else ""
+                    a_msg = interaction[1] if len(interaction) > 1 else ""
+                    # Paksa jadi string
+                    if isinstance(u_msg, list): u_msg = str(u_msg[0])
+                    if isinstance(a_msg, list): a_msg = str(a_msg[0])
+                    messages.append({"role": "user", "content": str(u_msg)})
+                    messages.append({"role": "assistant", "content": str(a_msg)})
+                    
+        user_input = message.get("text", "") if isinstance(message, dict) else str(message)
+        messages.append({"role": "user", "content": user_input})
+
+        # 4. Terapkan Template & Tokenisasi
         text_prompt = tokenizer.apply_chat_template(
             messages,
             tokenize=False,
             add_generation_prompt=True
         )
-        
-        inputs = tokenizer(text_prompt, return_tensors="pt").to("cpu")
+        inputs = tokenizer(text_prompt, return_tensors="pt").to("cpu") 
         
         with torch.no_grad():
             outputs = model.generate(