app.py

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

models = {
    "microsoft/Phi-3.5-vision-instruct": AutoModelForCausalLM.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True, torch_dtype="auto", _attn_implementation="flash_attention_2").cuda().eval()

}

processors = {
    "microsoft/Phi-3.5-vision-instruct": AutoProcessor.from_pretrained("microsoft/Phi-3.5-vision-instruct", trust_remote_code=True)
}

MARKDOWN = """
This demo utilizes <a href="https://huggingface.co/microsoft/Phi-3.5-vision-instruct">Phi-3.5-Vision Instruct</a> by @Microsoft.

Try out with different images and generate captions. Do provide your feedback.

Model Card is acquired from <a href="https://huggingface.co/microsoft/Phi-3.5-vision-instruct"> Microsoft's Phi Vision Instruct</a>

**Demo by [Sunder Ali Khowaja](https://sander-ali.github.io) - [X](https://x.com/SunderAKhowaja) -[Github](https://github.com/sander-ali) -[Hugging Face](https://huggingface.co/SunderAli17)**
"""

kwargs = {}
kwargs['torch_dtype'] = torch.bfloat16

promptu = '<|user|>\n'
prompta = '<|assistant|>\n'
prompts = "<|end|>\n"

@spaces.GPU
def run_example(image, text_input=None, model_id="microsoft/Phi-3.5-vision-instruct"):
    model = models[model_id]
    processor = processors[model_id]

    prompt = f"{promptu}<|image_1|>\n{text_input}{prompts}{prompta}"
    image = Image.fromarray(image).convert("RGB")

    inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
    generate_ids = model.generate(**inputs, 
                                max_new_tokens=1000,
                                eos_token_id=processor.tokenizer.eos_token_id,
                                )
    generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
    response = processor.batch_decode(generate_ids, 
                                    skip_special_tokens=True, 
                                    clean_up_tokenization_spaces=False)[0]
    return response

theme = gr.themes.Soft(
    font=[gr.themes.GoogleFont('Pacifico'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
)
js_func = """
function refresh() {
    const url = new URL(window.location);
    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""
with gr.Blocks(js=js_func, theme=theme) as demo:
    gr.Markdown(MARKDOWN)
    with gr.Tab(label="Phi-3.5 Input"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label="Input Picture")
                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="microsoft/Phi-3.5-vision-instruct")
                text_input = gr.Textbox(label="Question")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                output_text = gr.Textbox(label="Output Text")

        submit_btn.click(run_example, [input_img, text_input, model_selector], [output_text])

demo.launch(debug=True)