app.py

import gradio as gr
from transformers import AutoProcessor, Idefics2ForConditionalGeneration
import re
import time
from PIL import Image
import torch
import spaces
import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)


processor = AutoProcessor.from_pretrained("HuggingFaceM4/idefics2-8b")

model = Idefics2ForConditionalGeneration.from_pretrained(
        "HuggingFaceM4/idefics2-8b",
        torch_dtype=torch.bfloat16,
        #_attn_implementation="flash_attention_2",
        trust_remote_code=True).to("cuda")

@spaces.GPU(duration=180)
def model_inference(
    image, text, decoding_strategy, temperature,
    max_new_tokens, repetition_penalty, top_p
):
    if text == "" and not image:
        gr.Error("Please input a query and optionally image(s).")

    if text == "" and image:
        gr.Error("Please input a text query along the image(s).")

    resulting_messages = [
            {
                "role": "user",
                "content": [{"type": "image"}] + [
                    {"type": "text", "text": text}
                ]
            }
        ]


    prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
    inputs = processor(text=prompt, images=[image], return_tensors="pt")
    inputs = {k: v.to("cuda") for k, v in inputs.items()}

    generation_args = {
        "max_new_tokens": max_new_tokens,
        "repetition_penalty": repetition_penalty,

    }

    assert decoding_strategy in [
        "Greedy",
        "Top P Sampling",
    ]
    if decoding_strategy == "Greedy":
        generation_args["do_sample"] = False
    elif decoding_strategy == "Top P Sampling":
        generation_args["temperature"] = temperature
        generation_args["do_sample"] = True
        generation_args["top_p"] = top_p


    generation_args.update(inputs)

    # Generate
    generated_ids = model.generate(**generation_args)

    generated_texts = processor.batch_decode(generated_ids[:, generation_args["input_ids"].size(1):], skip_special_tokens=True)
    print("INPUT:", prompt, "|OUTPUT:", generated_texts)
    return generated_texts[0]


with gr.Blocks(fill_height=True) as demo:
    gr.Markdown("## IDEFICS2 Instruction 🐶")
    gr.Markdown("Play with [IDEFICS2-8B](https://huggingface.co/HuggingFaceM4/idefics2-8b) in this demo. To get started, upload an image and text or try one of the examples.")
    gr.Markdown("**Important note**: This model is not made for chatting, the chatty IDEFICS2 will be released in the upcoming days. **This model is very strong on various tasks, including visual question answering, document retrieval and more, you can see it through the examples.**")
    gr.Markdown("Learn more about IDEFICS2 in this [blog post](https://huggingface.co/blog/idefics2).")


    with gr.Column():
        image_input = gr.Image(label="Upload your Image", type="pil")
        query_input = gr.Textbox(label="Prompt")
        submit_btn = gr.Button("Submit")
        output = gr.Textbox(label="Output")

    with gr.Accordion(label="Example Inputs and Advanced Generation Parameters"):
        examples=[["./example_images/docvqa_example.png", "How many items are sold?", "Greedy", 0.4, 512, 1.2, 0.8],
                    ["./example_images/example_images_travel_tips.jpg", "I want to go somewhere similar to the one in the photo. Give me destinations and travel tips.", "Greedy", 0.4, 512, 1.2, 0.8],
                    ["./example_images/baklava.png", "Where is this pastry from?", "Greedy", 0.4, 512, 1.2, 0.8],
                    ["./example_images/dummy_pdf.png", "How much percent is the order status?", "Greedy", 0.4, 512, 1.2, 0.8],
                    ["./example_images/art_critic.png", "As an art critic AI assistant, could you describe this painting in details and make a thorough critic?.", "Greedy", 0.4, 512, 1.2, 0.8],
                    ["./example_images/s2w_example.png", "What is this UI about?", "Greedy", 0.4, 512, 1.2, 0.8]]

        # Hyper-parameters for generation
        max_new_tokens = gr.Slider(
              minimum=8,
              maximum=1024,
              value=512,
              step=1,
              interactive=True,
              label="Maximum number of new tokens to generate",
          )
        repetition_penalty = gr.Slider(
              minimum=0.01,
              maximum=5.0,
              value=1.2,
              step=0.01,
              interactive=True,
              label="Repetition penalty",
              info="1.0 is equivalent to no penalty",
          )
        temperature = gr.Slider(
              minimum=0.0,
              maximum=5.0,
              value=0.4,
              step=0.1,
              interactive=True,
              label="Sampling temperature",
              info="Higher values will produce more diverse outputs.",
          )
        top_p = gr.Slider(
              minimum=0.01,
              maximum=0.99,
              value=0.8,
              step=0.01,
              interactive=True,
              label="Top P",
              info="Higher values is equivalent to sampling more low-probability tokens.",
          )
        decoding_strategy = gr.Radio(
              [
                  "Greedy",
                  "Top P Sampling",
              ],
              value="Greedy",
              label="Decoding strategy",
              interactive=True,
              info="Higher values is equivalent to sampling more low-probability tokens.",
          )
        decoding_strategy.change(
              fn=lambda selection: gr.Slider(
                  visible=(
                      selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"]
                  )
              ),
              inputs=decoding_strategy,
              outputs=temperature,
          )

        decoding_strategy.change(
              fn=lambda selection: gr.Slider(
                  visible=(
                      selection in ["contrastive_sampling", "beam_sampling", "Top P Sampling", "sampling_top_k"]
                  )
              ),
              inputs=decoding_strategy,
              outputs=repetition_penalty,
          )
        decoding_strategy.change(
              fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
              inputs=decoding_strategy,
              outputs=top_p,
          )
        gr.Examples(
                        examples = examples,
                        inputs=[image_input, query_input, decoding_strategy, temperature,
                                                              max_new_tokens, repetition_penalty, top_p],
                        outputs=output,
                        fn=model_inference
                    )

        submit_btn.click(model_inference, inputs = [image_input, query_input, decoding_strategy, temperature,
                                                      max_new_tokens, repetition_penalty, top_p], outputs=output)


demo.launch(debug=True)