import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the inference client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define the response function
def respond(message, history, system_message, max_tokens, temperature, top_p):
    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]})

    messages.append({"role": "user", "content": message})
    
    response = ""
    
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

# Define the Gradio interface
demo = gr.Blocks()

with demo:
    # Load and display the HTML file
    with open("index.html", "r") as file:
        html_content = file.read()
    
    gr.HTML(html_content)
    
    # Create the chat interface
    gr.ChatInterface(
        respond,
        additional_inputs=[
            gr.Textbox(value="You are a friendly Chatbot.", 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)"),
        ],
    )

# Launch the application
if __name__ == "__main__":
    demo.launch()
import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the inference client
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define the response function
def respond(message, history, system_message, max_tokens, temperature, top_p):
    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]})

    messages.append({"role": "user", "content": message})
    
    response = ""
    
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

# Define the Gradio interface
demo = gr.Blocks()

with demo:
    # Load and display the HTML file
    with open("index.html", "r") as file:
        html_content = file.read()
    
    gr.HTML(html_content)
    
    # Create the chat interface
    gr.ChatInterface(
        respond,
        additional_inputs=[
            gr.Textbox(value="You are a friendly Chatbot.", 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)"),
        ],
    )

# Launch the application
if __name__ == "__main__":
    demo.launch()