Update app.py

app.py

@@ -2,49 +2,41 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 
 """
-For more information on `huggingface_hub` Inference API support, please check the docs: 
-https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+For more information on huggingface_hub Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
 client = InferenceClient("unsloth/Llama-3.2-1B-Instruct")
 
 
 def respond(
     message,
-    history: list[tuple[str, str]] = None  # Default history as None to avoid mutable issues
+    history: list[tuple[str, str]],
+    # system_message,
+    # max_tokens,
+    # temperature,
+    # top_p,
 ):
-    if history is None:
-        history = []
-
-    # System message describing the assistant's role
-    system_message = (
-        "You are a Dietician Assistant specializing in providing general guidance on diet, "
-        "nutrition, and healthy eating habits. Answer questions thoroughly with scientifically "
-        "backed advice, practical tips, and easy-to-understand explanations. Keep in mind that "
-        "your role is to assist, not replace a registered dietitian, so kindly remind users to "
-        "consult a professional for personalized advice when necessary."
-    )
-
-    # Define model parameters
+    
+    system_message = "You are a Dietician Assistant specializing in providing general guidance on diet, "
+    "nutrition, and healthy eating habits. Answer questions thoroughly with scientifically "
+    "backed advice, practical tips, and easy-to-understand explanations. Keep in mind that "
+    "your role is to assist, not replace a registered dietitian, so kindly remind users to "
+    "consult a professional for personalized advice when necessary."
     max_tokens = 512
     temperature = 0.7
     top_p = 0.95
-
-    # Initialize the message history with the system message
+    
     messages = [{"role": "system", "content": system_message}]
 
-    # Add previous history to the message chain
     for val in history:
         if val[0]:
             messages.append({"role": "user", "content": val[0]})
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
-    # Append the new user message
     messages.append({"role": "user", "content": message})
 
     response = ""
 
-    # Generate the response in a streaming fashion
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -53,10 +45,30 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
+
         response += token
         yield response
 
 
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+# demo = gr.ChatInterface(
+#     respond,
+#     # additional_inputs=[
+#     #     gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+#     #     gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+#     #     gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+#     #     gr.Slider(
+#     #         minimum=0.1,
+#     #         maximum=1.0,
+#     #         value=0.95,
+#     #         step=0.05,
+#     #         label="Top-p (nucleus sampling)",
+#     #     ),
+#     # ],
+# )
+
 def default_message():
     """Function to return initial default message."""
     return [("Hi there! I'm your Dietician Assistant, here to help with general advice "
@@ -65,11 +77,27 @@ def default_message():
 
 # Set up the Gradio ChatInterface with an initial default message
 with gr.Blocks() as demo:
-    chatbot = gr.ChatInterface(respond)
+    chatbot = gr.ChatInterface(
+        respond,
+        # additional_inputs=[
+        #     gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        #     gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        #     gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        #     gr.Slider(
+        #         minimum=0.1,
+        #         maximum=1.0,
+        #         value=0.95,
+        #         step=0.05,
+        #         label="Top-p (nucleus sampling)",
+        #     ),
+        # ],
+    )
     
     # Display the default message on load
     gr.State(default_message())  # Store initial chat history
     chatbot.history = default_message()  # Set the chat history to show the greeting
 
+
+
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()