Update app.py

app.py

@@ -2,10 +2,12 @@ import gradio as gr
 from openai import OpenAI
 import os
 
+# Load the Hugging Face access token from environment variables
 ACCESS_TOKEN = os.getenv("HF_TOKEN")
 
 print("Access token loaded.")
 
+# Initialize the OpenAI client with Hugging Face's serverless API
 client = OpenAI(
     base_url="https://api-inference.huggingface.co/v1/",
     api_key=ACCESS_TOKEN,
@@ -27,8 +29,10 @@ def respond(
     print(f"Received message: {message}")
     print(f"History: {history}")
     print(f"System message: {system_message}")
-    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}, Frequency Penalty: {frequency_penalty}, Top-K: {top_k}, Seed: {seed}")
+    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
+    print(f"Frequency penalty: {frequency_penalty}, Top-K: {top_k}, Seed: {seed}")
 
+    # Construct the messages list for the conversation context
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -62,10 +66,12 @@ def respond(
 
     print("Completed response generation.")
 
+# Initialize the chatbot interface
 chatbot = gr.Chatbot(height=600)
 
 print("Chatbot interface created.")
 
+# Create the Gradio interface with additional inputs for the new parameters
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
@@ -80,7 +86,7 @@ demo = gr.ChatInterface(
             label="Top-P",
         ),
         gr.Slider(
-            minimum=0.0,
+            minimum=-2.0,
             maximum=2.0,
             value=0.0,
             step=0.1,
@@ -95,10 +101,10 @@ demo = gr.ChatInterface(
         ),
         gr.Slider(
             minimum=-1,
-            maximum=10000,
+            maximum=2**31 - 1,
             value=-1,
             step=1,
-            label="Seed (-1 for random)",
+            label="Seed",
         ),
     ],
     fill_height=True,