app.py

import gradio as gr
<<<<<<< HEAD
from src.model.him_model import HIMModel
from src.core.config import HIMConfig

def initialize_model():
    config = HIMConfig()
    model = HIMModel(config)
    return model

def process_input(text: str, image: str = None, audio: str = None):
    input_data = {
        'text': text,
        'image': image,
        'audio': audio,
        'context': {}
    }
    
    result = model.forward(input_data)
    return format_output(result)

model = initialize_model()

interface = gr.Interface(
    fn=process_input,
    inputs=[
        gr.Textbox(label="Text Input"),
        gr.Image(label="Image Input", optional=True),
        gr.Audio(label="Audio Input", optional=True)
    ],
    outputs=[
        gr.Textbox(label="HIM Response"),
        gr.Plot(label="Consciousness State Visualization")
    ],
    title="Hybrid Intelligence Matrix (HIM)",
    description="Interact with the HIM system for advanced cognitive processing"
)
=======
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
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    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


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = 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)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()
>>>>>>> origin/main