Update app.py

app.py

@@ -1,16 +1,21 @@
 # app.py
+import os
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 
 MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.1"
 
+# Optional: get token from environment
+HF_TOKEN = os.getenv("HF_TOKEN")  # make sure you added this secret in HF Space
+
 # Load model & tokenizer
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
-model = AutoModelForCausalLM.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model = AutoModelForCausalLM.from_pretrained(
     MODEL_ID,
     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-    device_map="auto"
+    device_map="auto",
+    token=HF_TOKEN
 )
 
 def chat_with_mistral(prompt, history=None):
@@ -39,8 +44,8 @@ def chat_with_mistral(prompt, history=None):
     return answer, history
 
 
-with gr.Blocks(title="Mistral Chat (No API Key)") as app:
-    gr.Markdown("# 🐦 Mistral Chatbot (Offline Model)")
+with gr.Blocks(title="Mistral Chatbot") as app:
+    gr.Markdown("# 🐦 Mistral Chatbot (with HF Token)")
     chatbot = gr.Chatbot()
     msg = gr.Textbox(label="Your message")
     clear = gr.Button("Clear")
@@ -48,12 +53,11 @@ with gr.Blocks(title="Mistral Chat (No API Key)") as app:
     conversation_state = gr.State([])
 
     def handle_user_message(user_message, history):
-        # call model
         answer, new_history = chat_with_mistral(user_message, history)
         return "", new_history
     
     msg.submit(handle_user_message, [msg, conversation_state], [msg, chatbot, conversation_state])
-    clear.click(lambda: ([], []), None, [chatbot, conversation_state])
+    clear.click(lambda: ([], []), inputs=None, outputs=[chatbot, conversation_state])
 
 if __name__ == "__main__":
     app.launch()