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,
    custom_model,
    model,
    max_tokens,
    temperature,
    top_p,
    frequency_penalty,
    seed
):
    """
    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
    - custom_model: custom model path (if any)
    - model: selected model from featured models
    - 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'
    """

    print(f"Received message: {message}")
    print(f"History: {history}")
    print(f"System message: {system_message}")
    print(f"Custom model: {custom_model}")
    print(f"Selected model: {model}")
    print(f"Max tokens: {max_tokens}, Temperature: {temperature}, Top-P: {top_p}")
    print(f"Frequency Penalty: {frequency_penalty}, Seed: {seed}")

    # 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}]

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

    # Append the latest user message
    messages.append({"role": "user", "content": message})

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

    # Determine which model to use
    if custom_model.strip():
        selected_model = custom_model.strip()
    else:
        # Map the display names to actual model paths
        model_mapping = {
            "Llama 2 70B": "meta-llama/Llama-2-70b-chat-hf",
            "Mixtral 8x7B": "mistralai/Mixtral-8x7B-Instruct-v0.1",
            "Zephyr 7B": "HuggingFaceH4/zephyr-7b-beta",
            "OpenChat 3.5": "openchat/openchat-3.5-0106",
        }
        selected_model = model_mapping.get(model, "meta-llama/Llama-2-70b-chat-hf")

    # Make the streaming request to the HF Inference API via openai-like client
    for message_chunk in client.chat.completions.create(
        model=selected_model,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
        frequency_penalty=frequency_penalty,
        seed=seed,
        messages=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 response

    print("Completed response generation.")

# Create a Chatbot component with a specified height
chatbot = gr.Chatbot(height=600)
print("Chatbot interface created.")

# Create the Gradio interface with tabs
with gr.Blocks(theme="Nymbo/Nymbo_Theme") as demo:
    with gr.Row():
        with gr.Column():
            # Basic Settings Tab
            with gr.Tab("Settings"):
                # System Message
                system_message = gr.Textbox(
                    value="", 
                    label="System message",
                    placeholder="Enter a system message to guide the model's behavior"
                )
                
                # Model Selection Section
                with gr.Accordion("Featured Models", open=True):
                    # Model Search
                    model_search = gr.Textbox(
                        label="Filter Models",
                        placeholder="Search for a featured model...",
                        lines=1
                    )
                    
                    # Featured Models List
                    models_list = [
                        "Llama 2 70B",
                        "Mixtral 8x7B",
                        "Zephyr 7B",
                        "OpenChat 3.5"
                    ]
                    
                    model = gr.Radio(
                        label="Select a model",
                        choices=models_list,
                        value="Llama 2 70B"
                    )
                    
                    # Custom Model Input
                    custom_model = gr.Textbox(
                        label="Custom Model",
                        info="Hugging Face model path (optional)",
                        placeholder="meta-llama/Llama-2-70b-chat-hf"
                    )
                    
                    # Function to filter models
                    def filter_models(search_term):
                        filtered_models = [m for m in models_list if search_term.lower() in m.lower()]
                        return gr.update(choices=filtered_models)
                    
                    # Update model list when search box is used
                    model_search.change(filter_models, inputs=model_search, outputs=model)
                
                # Generation Parameters
                with gr.Row():
                    max_tokens = gr.Slider(
                        minimum=1,
                        maximum=4096,
                        value=512,
                        step=1,
                        label="Max new tokens"
                    )
                    temperature = gr.Slider(
                        minimum=0.1,
                        maximum=4.0,
                        value=0.7,
                        step=0.1,
                        label="Temperature"
                    )
                
                with gr.Row():
                    top_p = gr.Slider(
                        minimum=0.1,
                        maximum=1.0,
                        value=0.95,
                        step=0.05,
                        label="Top-P"
                    )
                    frequency_penalty = gr.Slider(
                        minimum=-2.0,
                        maximum=2.0,
                        value=0.0,
                        step=0.1,
                        label="Frequency Penalty"
                    )
                
                with gr.Row():
                    seed = gr.Slider(
                        minimum=-1,
                        maximum=65535,
                        value=-1,
                        step=1,
                        label="Seed (-1 for random)"
                    )
            
            # Information Tab
            with gr.Tab("Information"):
                # Featured Models Table
                with gr.Accordion("Featured Models", open=True):
                    gr.HTML(
                        """
                        <p><a href="https://huggingface.co/models?inference=warm&pipeline_tag=text-to-text">See all available models</a></p>
                        <table style="width:100%; text-align:center; margin:auto;">
                            <tr>
                                <th>Model Name</th>
                                <th>Size</th>
                                <th>Notes</th>
                            </tr>
                            <tr>
                                <td>Llama 2 70B</td>
                                <td>70B</td>
                                <td>Meta's flagship model</td>
                            </tr>
                            <tr>
                                <td>Mixtral 8x7B</td>
                                <td>47B</td>
                                <td>Mistral AI's MoE model</td>
                            </tr>
                            <tr>
                                <td>Zephyr 7B</td>
                                <td>7B</td>
                                <td>Efficient fine-tuned model</td>
                            </tr>
                            <tr>
                                <td>OpenChat 3.5</td>
                                <td>7B</td>
                                <td>High performance chat model</td>
                            </tr>
                        </table>
                        """
                    )
                
                # Parameters Overview
                with gr.Accordion("Parameters Overview", open=False):
                    gr.Markdown(
                        """
                        ## System Message
                        A message that sets the context and behavior for the model. This helps guide the model's responses.

                        ## Max New Tokens
                        Controls the maximum length of the generated response. Higher values allow for longer outputs but may take more time.

                        ## Temperature
                        Controls randomness in the output:
                        - Lower values (0.1-0.5): More focused and deterministic
                        - Higher values (0.7-1.0): More creative and diverse
                        - Very high values (>1.0): More random and potentially chaotic

                        ## Top-P (Nucleus Sampling)
                        Controls the cumulative probability threshold for token selection:
                        - Lower values: More focused on highly likely tokens
                        - Higher values: Considers a wider range of possibilities
                        
                        ## Frequency Penalty
                        Adjusts the likelihood of token repetition:
                        - Negative values: May encourage repetition
                        - Zero: Neutral
                        - Positive values: Discourages repetition

                        ## Seed
                        A number that controls the randomness in generation:
                        - -1: Random seed each time
                        - Fixed value: Reproducible outputs with same parameters
                        """
                    )

    # Set up the chat interface
    chatbot = gr.Chatbot(height=600)
    msg = gr.Textbox(label="Message")
    
    clear = gr.ClearButton([msg, chatbot])
    
    msg.submit(respond, [msg, chatbot, system_message, custom_model, model, max_tokens, temperature, top_p, frequency_penalty, seed], [chatbot, msg])

print("Launching the demo application.")
demo.launch(show_api=False, share=False)