app.py

import gradio as gr
import spaces
from huggingface_hub import InferenceClient
import torch
from transformers import AutoModelForCausalLM, ChameleonProcessor, AutoTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
from threading import Thread
from PIL import Image
import requests


model_path = "facebook/chameleon-7b"
# model = ChameleonForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
# processor = ChameleonProcessor.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
processor = ChameleonProcessor.from_pretrained(model_path)
tokenizer = processor.tokenizer

@spaces.GPU
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 = ""

    prompt = "I'm very intrigued by this work of art:<image>Please tell me about the artist."
    image = Image.open(requests.get("https://uploads4.wikiart.org/images/paul-klee/death-for-the-idea-1915.jpg!Large.jpg", stream=True).raw)

    inputs = processor(prompt, images=[image], return_tensors="pt").to(model.device, dtype=torch.bfloat16)
    # out = model.generate(**inputs, max_new_tokens=40, do_sample=False)

    streamer = TextIteratorStreamer(tokenizer)
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=20)

    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    partial_message = ""
    for new_token in streamer:
        partial_message += new_token
        yield partial_message


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    multimodal=True,
    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()