introduce auth

app.py

@@ -10,13 +10,16 @@ For more information on `huggingface_hub` Inference API support, please check th
 # Note change in instantiation***
 text_generator = pipeline("text-generation", model="google/gemma-2-2b")
 
-def respond(message, history, system_message, max_tokens, temperature, top_p):
+def authenticate_and_generate(token, message, history, system_message, max_tokens, temperature, top_p):
+    # Initialize the text-generation pipeline with the provided token
+    text_generator = pipeline("text-generation", model="google/gemma-2-2b", use_auth_token=token)
+    
     # Construct the prompt with system message, history, and user input
     prompt = system_message + "\n" + "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in history if msg[0] and msg[1]])
     prompt += f"\nUser: {message}\nAssistant:"
 
     # Generate a response using the model
-    response = text_generator(prompt, max_length=max_tokens, temperature=temperature, top_p=top_p, do_sample=True, truncation=False)
+    response = text_generator(prompt, max_length=max_tokens, temperature=temperature, top_p=top_p, do_sample=True, truncation=True)
 
     # Extract the generated text from the response list
     assistant_response = response[0]['generated_text']
@@ -28,8 +31,15 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
 athena = gr.ChatInterface(
-    fn=respond,
+    fn=authenticate_and_generate,
     additional_inputs=[
+        gr.Textbox(
+            label="Hugging Face API Token",
+            type="password",
+            placeholder="Please provide a Hugging Face auth token.",
+            lines=1,
+            max_lines=1
+        ),
         gr.Textbox(value=
                    """
                    You are a marketing-minded content writer for Plan.com (a UK telecommunications company).