gradio text box xhange

app.py

@@ -178,26 +178,27 @@ def gradio_rag_wrapper(message, history, token):
     
     return response
 
-def save_token(token):
-    return token
-
 # Create the Gradio interface with multimodal input
 with gr.Blocks(title="Contextual RAG Chatbot on Hugging Face Spaces") as demo:
     gr.Markdown("## Contextual RAG Chatbot")
     gr.Markdown("Please enter your Hugging Face Access Token to access gated models like Llama 3.2. You can generate a token from your [Hugging Face settings](https://huggingface.co/settings/tokens).")
 
-    token_input = gr.Textbox(label="Hugging Face Access Token")
-    save_btn = gr.Button("Save Token")
-    token_state = gr.State("")  # hidden state to store token
-    save_btn.click(fn=save_token, inputs=token_input, outputs=token_state)
+    newToken = ""
+
+    def tokenFunc (value) :
+        newToken = value
+
+    name = gr.Textbox(
+        label="Hugging Face Access Token",
+    )
+
+    print("toeken =---------->[0]", name.value)
     
-    # 2. Chatbot
     chatbot = gr.ChatInterface(
-        fn=lambda message, history, token: gradio_rag_wrapper(message, history, token),
-        state=token_state,  # pass token state to the function
+        fn=lambda message, history: gradio_rag_wrapper(message, history, name.value),
         multimodal=True,
         description="Upload a PDF file to start chatting!",
-        textbox=gr.MultimodalTextbox(file_types=[".pdf"], show_submit=True)
+        textbox=gr.MultimodalTextbox(file_types=[".pdf"]),
     )
 
 if __name__ == "__main__":