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)
}

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]."

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

@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 = """
  #container {
    border: 2px solid #333;
    padding: 20px;
    max-width: 400px;
    margin: auto;
  }
  #input_img, #output_text {
    border: 1px solid #444;
    border-radius: 5px;
  }
  #input_img {
    height: 200px;
    overflow: hidden;
  }
  #output_text {
    height: 150px;
    overflow-y: auto;
  }
  .copy-btn {
    display: inline-block;
    padding: 5px 10px;
    font-size: 14px;
    background-color: #333;
    color: #fff;
    border: none;
    border-radius: 3px;
    cursor: pointer;
    margin-top: 10px;
  }
"""

with gr.Blocks(css=css) as demo:
    with gr.Box(elem_id="container"):
        input_img = gr.Image(label="Input Picture", elem_id="input_img")
        text_input = gr.Textbox(value=default_question, visible=False)
        submit_btn = gr.Button(value="Generate")
        output_text = gr.Textbox(label="Output Text", elem_id="output_text")
        
        submit_btn.click(run_example, [input_img, text_input], [output_text])

    def copy_to_clipboard(content):
        import pyperclip
        pyperclip.copy(content)
        return "Text copied!"

    copy_button = gr.Button("Copy Text", elem_id="copy-btn")
    copy_button.click(copy_to_clipboard, inputs=output_text, outputs=None)

demo.queue(api_open=False)
demo.launch(debug=True, show_api=False)