Update app.py

app.py

@@ -2,16 +2,18 @@ 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 pointing to the Hugging Face Inference API
 client = OpenAI(
     base_url="https://api-inference.huggingface.co/v1/",
     api_key=ACCESS_TOKEN,
 )
 print("OpenAI client initialized.")
 
-
+# Define the main function that handles chat responses
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -21,7 +23,8 @@ def respond(
     top_p,
     frequency_penalty,
     seed,
-    custom_model
+    custom_model,  # Input from the Custom Model textbox
+    featured_model # Input from the Featured Model radio buttons <<< NEW INPUT
 ):
 
     print(f"Received message: {message}")
@@ -29,16 +32,18 @@ def respond(
     print(f"System message: {system_message}")
     print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
     print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
-    print(f"Selected model (custom_model): {custom_model}")
+    print(f"Custom model input: '{custom_model}'")
+    print(f"Selected featured model: {featured_model}") # Log the featured model selection
 
     # Convert seed to None if -1 (meaning random)
     if seed == -1:
         seed = None
 
+    # Start constructing the message list for the API call with the system message
     messages = [{"role": "system", "content": system_message}]
     print("Initial messages array constructed.")
 
-    # Add conversation history to the context
+    # Add the conversation history to the messages list
     for val in history:
         user_part = val[0]
         assistant_part = val[1]
@@ -49,97 +54,119 @@ def respond(
             messages.append({"role": "assistant", "content": assistant_part})
             print(f"Added assistant message to context: {assistant_part}")
 
-    # Append the latest user message
+    # Add the latest user message to the list
     messages.append({"role": "user", "content": message})
     print("Latest user message appended.")
 
-    # If user provided a model, use that; otherwise, fall back to a default model
-    model_to_use = custom_model.strip() if custom_model.strip() != "" else "meta-llama/Llama-3.3-70B-Instruct"
-    print(f"Model selected for inference: {model_to_use}")
+    # <<< MODEL SELECTION LOGIC UPDATED >>>
+    # Determine the model to use: Prioritize the custom model box if it's filled,
+    # otherwise use the selected featured model.
+    custom_model_stripped = custom_model.strip() # Remove leading/trailing whitespace
+    if custom_model_stripped != "":
+        model_to_use = custom_model_stripped # Use custom model if provided
+        print(f"Using custom model: {model_to_use}")
+    else:
+        model_to_use = featured_model # Use the selected featured model
+        print(f"Using selected featured model: {model_to_use}")
+
 
-    # Start with an empty string to build the response as tokens stream in
+    # Initialize an empty string to accumulate the response tokens
     response = ""
-    print("Sending request to OpenAI API.")
+    print("Sending request to Hugging Face Inference API.")
 
+    # Stream the response from the API
     for message_chunk in client.chat.completions.create(
-        model=model_to_use,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-        frequency_penalty=frequency_penalty,
-        seed=seed,
-        messages=messages,
+        model=model_to_use,          # Use the determined model
+        max_tokens=max_tokens,       # Set maximum tokens for the response
+        stream=True,                 # Enable streaming responses
+        temperature=temperature,     # Set sampling temperature
+        top_p=top_p,                 # Set nucleus sampling probability
+        frequency_penalty=frequency_penalty, # Set frequency penalty
+        seed=seed,                   # Set random seed (if provided)
+        messages=messages,           # Pass the constructed message history
     ):
+        # Get the text content from the current chunk
         token_text = message_chunk.choices[0].delta.content
-        print(f"Received token: {token_text}")
-        response += token_text
-        yield response
+        # Append the token text to the response string (if it's not None)
+        if token_text:
+            print(f"Received token: {token_text}")
+            response += token_text
+            yield response # Yield the partial response back to Gradio for live updates
 
     print("Completed response generation.")
 
-# GRADIO UI
+# --- GRADIO UI ---
 
+# Create the main chatbot display area
 chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", layout="panel")
 print("Chatbot interface created.")
 
+# Create the System Prompt input box
 system_message_box = gr.Textbox(value="", placeholder="You are a helpful assistant.", label="System Prompt")
 
-max_tokens_slider = gr.Slider(
-    minimum=1,
-    maximum=4096,
-    value=512,
-    step=1,
-    label="Max new tokens"
-)
-temperature_slider = gr.Slider(
-    minimum=0.1,
-    maximum=4.0,
-    value=0.7,
-    step=0.1,
-    label="Temperature"
-)
-top_p_slider = gr.Slider(
-    minimum=0.1,
-    maximum=1.0,
-    value=0.95,
-    step=0.05,
-    label="Top-P"
-)
-frequency_penalty_slider = gr.Slider(
-    minimum=-2.0,
-    maximum=2.0,
-    value=0.0,
-    step=0.1,
-    label="Frequency Penalty"
-)
-seed_slider = gr.Slider(
-    minimum=-1,
-    maximum=65535,
-    value=-1,
-    step=1,
-    label="Seed (-1 for random)"
-)
+# Create sliders for model parameters
+max_tokens_slider = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max new tokens")
+temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P")
+frequency_penalty_slider = gr.Slider(minimum=-2.0, maximum=2.0, value=0.0, step=0.1, label="Frequency Penalty")
+seed_slider = gr.Slider(minimum=-1, maximum=65535, value=-1, step=1, label="Seed (-1 for random)")
 
-# The custom_model_box is what the respond function sees as "custom_model"
+# Create the Custom Model input box
 custom_model_box = gr.Textbox(
-    value="",
+    value="", # Default to empty
     label="Custom Model",
-    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model.",
-    placeholder="meta-llama/Llama-3.3-70B-Instruct"
+    info="(Optional) Provide a custom Hugging Face model path. Overrides the featured model selection below.",
+    placeholder="e.g., username/my-custom-model" # Updated placeholder
+)
+
+# Define the list of featured models
+models_list = [
+    "meta-llama/Llama-3.3-70B-Instruct", # Default selected model
+    "meta-llama/Llama-3.1-70B-Instruct",
+    "meta-llama/Llama-3.0-70B-Instruct",
+    "meta-llama/Llama-3.2-3B-Instruct",
+    "meta-llama/Llama-3.2-1B-Instruct",
+    "meta-llama/Llama-3.1-8B-Instruct",
+    "NousResearch/Hermes-3-Llama-3.1-8B",
+    "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
+    "mistralai/Mistral-Nemo-Instruct-2407",
+    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "mistralai/Mistral-7B-Instruct-v0.3",
+    "mistralai/Mistral-7B-Instruct-v0.2",
+    "Qwen/Qwen3-235B-A22B",
+    "Qwen/Qwen3-32B",
+    "Qwen/Qwen2.5-72B-Instruct",
+    "Qwen/Qwen2.5-3B-Instruct",
+    "Qwen/Qwen2.5-0.5B-Instruct",
+    "Qwen/QwQ-32B",
+    "Qwen/Qwen2.5-Coder-32B-Instruct",
+    "microsoft/Phi-3.5-mini-instruct",
+    "microsoft/Phi-3-mini-128k-instruct",
+    "microsoft/Phi-3-mini-4k-instruct",
+    "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+    "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+    "HuggingFaceH4/zephyr-7b-beta",
+    "HuggingFaceTB/SmolLM2-360M-Instruct",
+    "tiiuae/falcon-7b-instruct",
+    "01-ai/Yi-1.5-34B-Chat",
+]
+print("Models list initialized.")
+
+# Create the radio button selector for featured models
+featured_model_radio = gr.Radio(
+    label="Select a Featured Model", # Changed label slightly
+    choices=models_list,
+    value="meta-llama/Llama-3.3-70B-Instruct", # Set the default selection
+    interactive=True
 )
+print("Featured models radio button created.")
 
-def set_custom_model_from_radio(selected):
-    """
-    This function will get triggered whenever someone picks a model from the 'Featured Models' radio.
-    We will update the Custom Model text box with that selection automatically.
-    """
-    print(f"Featured model selected: {selected}")
-    return selected
 
+# --- Create the main Chat Interface ---
+# <<< `additional_inputs` UPDATED >>>
 demo = gr.ChatInterface(
-    fn=respond,
-    additional_inputs=[
+    fn=respond, # The function to call when a message is sent
+    additional_inputs=[ # List of input components passed to the 'respond' function
         system_message_box,
         max_tokens_slider,
         temperature_slider,
@@ -147,84 +174,73 @@ demo = gr.ChatInterface(
         frequency_penalty_slider,
         seed_slider,
         custom_model_box,
+        featured_model_radio # Pass the radio button selection <<< ADDED
     ],
-    fill_height=True,
-    chatbot=chatbot,
-    theme="Nymbo/Nymbo_Theme",
+    fill_height=True, # Make the interface fill the available height
+    chatbot=chatbot, # Use the predefined chatbot component
+    theme="Nymbo/Nymbo_Theme", # Apply a theme
 )
 print("ChatInterface object created.")
 
-with demo:
-    with gr.Accordion("Model Selection", open=False):
+# --- Add Model Selection Controls within the Interface ---
+with demo: # Use the ChatInterface as a context manager to add elements
+    with gr.Accordion("Model Selection & Parameters", open=False): # Group controls in an accordion
+        # --- Featured Model Selection ---
+        gr.Markdown("### Featured Models") # Section title
         model_search_box = gr.Textbox(
             label="Filter Models",
-            placeholder="Search for a featured model...",
+            placeholder="Search featured models...",
             lines=1
         )
         print("Model search box created.")
 
-        models_list = [
-            "meta-llama/Llama-3.3-70B-Instruct",
-            "meta-llama/Llama-3.1-70B-Instruct",
-            "meta-llama/Llama-3.0-70B-Instruct",
-            "meta-llama/Llama-3.2-3B-Instruct",
-            "meta-llama/Llama-3.2-1B-Instruct",
-            "meta-llama/Llama-3.1-8B-Instruct",
-            "NousResearch/Hermes-3-Llama-3.1-8B",
-            "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
-            "mistralai/Mistral-Nemo-Instruct-2407",
-            "mistralai/Mixtral-8x7B-Instruct-v0.1",
-            "mistralai/Mistral-7B-Instruct-v0.3",
-            "mistralai/Mistral-7B-Instruct-v0.2",
-            "Qwen/Qwen3-235B-A22B",
-            "Qwen/Qwen3-32B",
-            "Qwen/Qwen2.5-72B-Instruct",
-            "Qwen/Qwen2.5-3B-Instruct",
-            "Qwen/Qwen2.5-0.5B-Instruct",
-            "Qwen/QwQ-32B",
-            "Qwen/Qwen2.5-Coder-32B-Instruct",
-            "microsoft/Phi-3.5-mini-instruct",
-            "microsoft/Phi-3-mini-128k-instruct",
-            "microsoft/Phi-3-mini-4k-instruct",
-            "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
-            "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
-            "HuggingFaceH4/zephyr-7b-beta",
-            "HuggingFaceTB/SmolLM2-360M-Instruct",
-            "tiiuae/falcon-7b-instruct",
-            "01-ai/Yi-1.5-34B-Chat",
-        ]
-        print("Models list initialized.")
-
-        featured_model_radio = gr.Radio(
-            label="Select a model below",
-            choices=models_list,
-            value="meta-llama/Llama-3.3-70B-Instruct",
-            interactive=True
-        )
-        print("Featured models radio button created.")
+        # Place the radio buttons here
+        # No need to define `featured_model_radio` again, just use the variable defined above
+        demo.load(lambda: featured_model_radio, outputs=featured_model_radio) # Ensure it appears in the layout
+        print("Featured model radio added to layout.")
+
+
+        # --- Custom Model Input ---
+        gr.Markdown("### Custom Model") # Section title
+        # No need to define `custom_model_box` again, just use the variable defined above
+        demo.load(lambda: custom_model_box, outputs=custom_model_box) # Ensure it appears in the layout
+        print("Custom model box added to layout.")
 
+        # --- Parameters ---
+        gr.Markdown("### Parameters") # Section title
+        # Add sliders to the layout
+        demo.load(lambda: max_tokens_slider, outputs=max_tokens_slider)
+        demo.load(lambda: temperature_slider, outputs=temperature_slider)
+        demo.load(lambda: top_p_slider, outputs=top_p_slider)
+        demo.load(lambda: frequency_penalty_slider, outputs=frequency_penalty_slider)
+        demo.load(lambda: seed_slider, outputs=seed_slider)
+        print("Parameter sliders added to layout.")
+
+
+        # --- Event Listeners ---
+
+        # Function to filter the radio button choices based on search input
         def filter_models(search_term):
             print(f"Filtering models with search term: {search_term}")
+            # List comprehension to find models matching the search term (case-insensitive)
             filtered = [m for m in models_list if search_term.lower() in m.lower()]
             print(f"Filtered models: {filtered}")
+            # Update the 'choices' property of the radio button component
             return gr.update(choices=filtered)
 
+        # Link the search box's 'change' event to the filter function
         model_search_box.change(
-            fn=filter_models,
-            inputs=model_search_box,
-            outputs=featured_model_radio
+            fn=filter_models,          # Function to call
+            inputs=model_search_box,   # Input component triggering the event
+            outputs=featured_model_radio # Output component to update
         )
         print("Model search box change event linked.")
 
-        featured_model_radio.change(
-            fn=set_custom_model_from_radio,
-            inputs=featured_model_radio,
-            outputs=custom_model_box
-        )
-        print("Featured model radio button change event linked.")
 
-print("Gradio interface initialized.")
+print("Gradio interface layout defined.")
 
+# --- Launch the Application ---
 if __name__ == "__main__":
-    print("Launching the demo application.")
+    print("Launching the Gradio demo application.")
+    # Launch the Gradio app with API endpoint enabled
     demo.launch(show_api=True)