gradio text box xhange

app.py

@@ -183,22 +183,33 @@ 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).")
 
-    newToken = ""
-
-    def tokenFunc (value) :
-        newToken = value
-
-    name = gr.Textbox(
+    hf_token_textbox = gr.Textbox(
         label="Hugging Face Access Token",
+        type="password",
+        interactive=True
     )
-
-    print("toeken =---------->[0]", name.value)
     
-    chatbot = gr.ChatInterface(
-        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"]),
+    # Use gr.Chatbot and gr.MultimodalTextbox for more control
+    chatbot = gr.Chatbot(label="Chatbot")
+    
+    msg = gr.MultimodalTextbox(
+        placeholder="Upload a PDF file or enter your query...", 
+        file_types=[".pdf"],
+        interactive=True
+    )
+    
+    # Submit handler to process user input and update the chatbot
+    def respond(message, chat_history, hf_token_from_textbox):
+        # The wrapper function now correctly receives the token from the text box
+        response = gradio_rag_wrapper(message, chat_history, hf_token_from_textbox)
+        chat_history.append((message, response))
+        return "", chat_history
+    
+    # Define the submit event to call the respond function
+    msg.submit(
+        respond,
+        inputs=[msg, chatbot, hf_token_textbox],
+        outputs=[msg, chatbot],
     )
 
 if __name__ == "__main__":