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)
}

DESCRIPTION = " "

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

user_prompt = '<|user|>\n'
assistant_prompt = '<|assistant|>\n'
prompt_suffix = "<|end|>\n"

default_question = "You are an image to prompt converter. Your work is to observe each and every detail of the image and craft a detailed prompt under 100 words in this format: [image content/subject, description of action, state, and mood], [art form, style], [artist/photographer reference if needed], [additional settings such as camera and lens settings, lighting, colors, effects, texture, background, rendering]."

@spaces.GPU
def run_example(image, text_input=default_question, model_id="microsoft/Phi-3.5-vision-instruct"):
    model = models[model_id]
    processor = processors[model_id]

    prompt = f"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
    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

css = """
  #output {
    margin-top: 15px;
    border: 2px solid #333; /* Darker outline */
    border-radius: 8px;
    height: 180px; /* Fixed height */
    object-fit: contain; /* Ensure image fits within the fixed height */ 
  }

   #input_img {
    margin-top: 15px;
    border: 2px solid #333; /* Darker outline */
    border-radius: 8px;
    height: 180px; /* Fixed height */
    object-fit: contain; /* Ensure image fits within the fixed height */
  }
  #model_selector, #text_input { 
    display: none !important; 
  }
"""

with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)
    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", visible=False)
                text_input = gr.Textbox(label="Question", value=default_question, visible=False)
                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.queue(api_open=False)
demo.launch(debug=True, show_api=False)