Update app.py

app.py

@@ -6,9 +6,9 @@ from functools import lru_cache
 def load_hf_model(model_name):
     # Use the Gradio-built huggingface loader instead of transformers_gradio
     return gr.load(
-        name=f"huggingface/deepseek-ai/{model_name}",
+        name=f"deepseek-ai/{model_name}",
         src="huggingface",  # Changed from transformers_gradio.registry
-        api_name="chat",
+        api_name="/chat"
     )
 
 # Load all models at startup
@@ -21,31 +21,32 @@ MODELS = {
 # --- Chatbot function ---
 def chatbot(input_text, history, model_choice, system_message, max_new_tokens, temperature, top_p):
     history = history or []
-
+    
     # Get the selected model component
     model_component = MODELS[model_choice]
-
+    
     # Create payload for the model
-    payload = [
-        history,  # Pass the entire history 
-        input_text,
-        system_message,
-        max_new_tokens,
-        temperature,
-        top_p
-    ]
-
+    payload = {
+        "messages": [{"role": "user", "content": input_text}],
+        "system": system_message,
+        "max_tokens": max_new_tokens,
+        "temperature": temperature,
+        "top_p": top_p
+    }
+    
     # Run inference using the selected model
     try:
-        response = model_component(payload) # the response now it is a tuple containing the updated history as the first element and the generated text as the second
-        updated_history, assistant_response = response[0], response[1]
-
-        history = updated_history
-
+        response = model_component(payload)  # The response is likely a dictionary
+        if isinstance(response, dict) and "choices" in response:
+            assistant_response = response["choices"][0]["message"]["content"]
+        else:
+            assistant_response = "Unexpected model response format."
     except Exception as e:
         assistant_response = f"Error: {str(e)}"
-        history.append((input_text, assistant_response))
-
+    
+    # Append user and assistant messages to history
+    history.append((input_text, assistant_response))
+    
     return history, history, ""
 
 # --- Gradio Interface ---