app.py

import gradio as gr
from openai import OpenAI
import os

# Retrieve the access token from the environment variable
ACCESS_TOKEN = os.getenv("HF_TOKEN")
print("Access token loaded.")

# Initialize the OpenAI client with the Hugging Face Inference API endpoint
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)
print("OpenAI client initialized.")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    frequency_penalty,
    seed,
    custom_model
):
    """
    This function handles the chatbot response. It takes in:
    - message: the user's new message
    - history: the list of previous messages, each as a tuple (user_msg, assistant_msg)
    - system_message: the system prompt
    - max_tokens: the maximum number of tokens to generate in the response
    - temperature: sampling temperature
    - top_p: top-p (nucleus) sampling
    - frequency_penalty: penalize repeated tokens in the output
    - seed: a fixed seed for reproducibility; -1 will mean 'random'
    - custom_model: the final model name in use, which may be set by selecting from the Featured Models radio or by typing a custom model
    """

    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}")
    print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")
    print(f"Selected model (custom_model): {custom_model}")

    # Convert seed to None if -1 (meaning random)
    if seed == -1:
        seed = None

    # Construct the messages array required by the API
    messages = [{"role": "system", "content": system_message}]
    print("Initial messages array constructed.")

    # Add conversation history to the context
    for val in history:
        user_part = val[0]  # Extract user message from the tuple
        assistant_part = val[1]  # Extract assistant message from the tuple
        if user_part:
            messages.append({"role": "user", "content": user_part})  # Append user message
            print(f"Added user message to context: {user_part}")
        if assistant_part:
            messages.append({"role": "assistant", "content": assistant_part})  # Append assistant message
            print(f"Added assistant message to context: {assistant_part}")

    # Append the latest user message
    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}")

    # Start with an empty string to build the response as tokens stream in
    response = ""
    print("Sending request to OpenAI API.")

    # Make the streaming request to the HF Inference API via openai-like client
    for message_chunk in client.chat.completions.create(
        model=model_to_use,              # Use either the user-provided or default model
        max_tokens=max_tokens,           # Maximum tokens for the response
        stream=True,                     # Enable streaming responses
        temperature=temperature,         # Adjust randomness in response
        top_p=top_p,                     # Control diversity in response generation
        frequency_penalty=frequency_penalty,  # Penalize repeated phrases
        seed=seed,                       # Set random seed for reproducibility
        messages=messages,               # Contextual conversation messages
    ):
        # Extract the token text from the response chunk
        token_text = message_chunk.choices[0].delta.content
        print(f"Received token: {token_text}")
        response += token_text
        # Yield the partial response to Gradio so it can display in real-time
        yield response

    print("Completed response generation.")

# -------------------------
# GRADIO UI CONFIGURATION
# -------------------------

# Create a Chatbot component with a specified height
chatbot = gr.Chatbot(height=600, show_copy_button=True, placeholder="Select a model and begin chatting", likeable=True, layout="panel")  # Define the height of the chatbot interface
print("Chatbot interface created.")

# Create textboxes and sliders for system prompt, tokens, and other parameters
system_message_box = gr.Textbox(value="", label="System message")  # Input box for system message

max_tokens_slider = gr.Slider(
    minimum=1,   # Minimum allowable tokens
    maximum=4096,  # Maximum allowable tokens
    value=512,   # Default value
    step=1,      # Increment step size
    label="Max new tokens"  # Slider label
)
temperature_slider = gr.Slider(
    minimum=0.1,  # Minimum temperature
    maximum=4.0,  # Maximum temperature
    value=0.7,    # Default value
    step=0.1,     # Increment step size
    label="Temperature"  # Slider label
)
top_p_slider = gr.Slider(
    minimum=0.1,  # Minimum top-p value
    maximum=1.0,  # Maximum top-p value
    value=0.95,   # Default value
    step=0.05,    # Increment step size
    label="Top-P"  # Slider label
)
frequency_penalty_slider = gr.Slider(
    minimum=-2.0,  # Minimum penalty
    maximum=2.0,   # Maximum penalty
    value=0.0,     # Default value
    step=0.1,      # Increment step size
    label="Frequency Penalty"  # Slider label
)
seed_slider = gr.Slider(
    minimum=-1,    # -1 for random seed
    maximum=65535, # Maximum seed value
    value=-1,      # Default value
    step=1,        # Increment step size
    label="Seed (-1 for random)"  # Slider label
)

# The custom_model_box is what the respond function sees as "custom_model"
custom_model_box = gr.Textbox(
    value="",  # Default value
    label="Custom Model",  # Label for the textbox
    info="(Optional) Provide a custom Hugging Face model path. Overrides any selected featured model."  # Additional info
)

# Define a function that updates the custom model box when a featured model is selected
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}")  # Log selected model
    return selected

# Create the main ChatInterface object
demo = gr.ChatInterface(
    fn=respond,  # The function to handle responses
    additional_inputs=[
        system_message_box,          # System message input
        max_tokens_slider,           # Max tokens slider
        temperature_slider,          # Temperature slider
        top_p_slider,                # Top-P slider
        frequency_penalty_slider,    # Frequency penalty slider
        seed_slider,                 # Seed slider
        custom_model_box             # Custom model input
    ],
    fill_height=True,  # Allow the chatbot to fill the container height
    chatbot=chatbot,   # Chatbot UI component
    textbox=gr.MultimodalTextbox(),
    multimodal=True,
    concurrency_limit=20,
    theme="Nymbo/Nymbo_Theme",  # Theme for the interface
    examples=[{"text": "Howdy, partner!",},
              {"text": "What's your model name and who trained you?",},
              {"text": "How many R's are there in the word Strawberry?"},],
    cache_examples=False
)

print("ChatInterface object created.")

# -----------
# ADDING THE "FEATURED MODELS" ACCORDION
# -----------
with demo:
    with gr.Accordion("Featured Models", open=False):  # Collapsible section for featured models
        model_search_box = gr.Textbox(
            label="Filter Models",  # Label for the search box
            placeholder="Search for a featured model...",  # Placeholder text
            lines=1  # Single-line input
        )
        print("Model search box created.")

        # Sample list of popular text models
        models_list = [
            "meta-llama/Llama-3.3-70B-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",
            "google/gemma-2-27b-it",
            "google/gemma-2-9b-it",
            "google/gemma-2-2b-it",
            "mistralai/Mistral-Nemo-Instruct-2407",
            "mistralai/Mixtral-8x7B-Instruct-v0.1",
            "mistralai/Mistral-7B-Instruct-v0.3",
            "Qwen/Qwen2.5-72B-Instruct",
            "Qwen/QwQ-32B-Preview",
            "PowerInfer/SmallThinker-3B-Preview",
            "HuggingFaceTB/SmolLM2-1.7B-Instruct",
            "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
            "microsoft/Phi-3.5-mini-instruct",
        ]
        print("Models list initialized.")

        featured_model_radio = gr.Radio(
            label="Select a model below",  # Label for the radio buttons
            choices=models_list,  # List of available models
            value="meta-llama/Llama-3.3-70B-Instruct",  # Default selection
            interactive=True  # Allow user interaction
        )
        print("Featured models radio button created.")

        # Filter function for the radio button list
        def filter_models(search_term):
            print(f"Filtering models with search term: {search_term}")  # Log the search term
            filtered = [m for m in models_list if search_term.lower() in m.lower()]  # Filter models by search term
            print(f"Filtered models: {filtered}")  # Log filtered models
            return gr.update(choices=filtered)

        # Update the radio list when the search box value changes
        model_search_box.change(
            fn=filter_models,  # Function to filter models
            inputs=model_search_box,  # Input: search box value
            outputs=featured_model_radio  # Output: update radio button list
        )
        print("Model search box change event linked.")

        # Update the custom model textbox when a featured model is selected
        featured_model_radio.change(
            fn=set_custom_model_from_radio,  # Function to set custom model
            inputs=featured_model_radio,  # Input: selected model
            outputs=custom_model_box  # Output: update custom model textbox
        )
        print("Featured model radio button change event linked.")

print("Gradio interface initialized.")

if __name__ == "__main__":
    print("Launching the demo application.")
    demo.launch()