Update app.py

app.py

@@ -1,32 +1,42 @@
 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
-"""
+# Hugging Face client initialization
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+# Function to handle NLP responses and interaction with the model
+def respond(message, history, system_message, max_tokens, temperature, top_p):
+    """
+    Function to handle user message and generate a response using the NLP model.
+    
+    Parameters:
+    message (str): User's current message/input.
+    history (list): List of tuples representing conversation history (user's and assistant's messages).
+    system_message (str): System-level instructions to the assistant to guide its responses.
+    max_tokens (int): Maximum number of tokens to generate in the response.
+    temperature (float): Degree of randomness in the response generation.
+    top_p (float): Controls the diversity of the response using nucleus sampling.
+    
+    Yields:
+    str: Streamed response as tokens are generated.
+    """
+    # Prepare the message for the assistant, including system-level instructions and history.
     messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
+    
+    # Loop through the history and add past conversation to the messages
+    for user_message, assistant_message in history:
+        if user_message:
+            messages.append({"role": "user", "content": user_message})
+        if assistant_message:
+            messages.append({"role": "assistant", "content": assistant_message})
+    
+    # Append the current user message to the conversation
     messages.append({"role": "user", "content": message})
 
+    # Initialize the response variable
     response = ""
 
+    # Get the response stream from the Hugging Face model
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -34,15 +44,12 @@ def respond(
         temperature=temperature,
         top_p=top_p,
     ):
+        # Extract the token content and append it to the response
         token = message.choices[0].delta.content
-
         response += token
         yield response
 
-
-"""
-System Prompt Modification for NLPToolkit Agent
-"""
+# System prompt to guide the assistant's behavior
 default_system_message = (
     "You are NLPToolkit Agent, an advanced natural language processing assistant. "
     "You specialize in tasks such as text summarization, sentiment analysis, text classification, "
@@ -50,39 +57,49 @@ default_system_message = (
     "Assist users with clear, concise, and actionable outputs."
 )
 
-"""
-Updated Gradio Interface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(
-            value=default_system_message, 
-            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)"
-        ),
-    ],
-)
+# Create the Gradio interface for user interaction
+def create_interface():
+    """
+    Create and return a Gradio interface for the NLPToolkit Agent with customizable parameters.
+    
+    Parameters:
+    None
+    
+    Returns:
+    gr.Interface: The Gradio interface object.
+    """
+    return gr.ChatInterface(
+        respond,
+        additional_inputs=[
+            gr.Textbox(
+                value=default_system_message, 
+                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)"
+            ),
+        ],
+    )
 
-# Run the app
-demo.launch()
+# Run the Gradio interface
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()