app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
import torch
from PIL import Image

# Model ve işlemci yükleme
models = {
    "microsoft/Phi-3.5-vision-instruct": AutoModelForCausalLM.from_pretrained(
        "microsoft/Phi-3.5-vision-instruct", 
        trust_remote_code=True, 
        torch_dtype=torch.float32,  # CPU üzerinde çalıştığı için float32 kullanılıyor
        device_map="auto",  # FlashAttention2 kontrolünü devre dışı bırakır
        low_cpu_mem_usage=True  # Daha az bellek kullanımı sağlar
    ).eval()
}

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

DESCRIPTION = "[Phi-3.5-vision Demo](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)"

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

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"{user_prompt}<|image_1|>\n{text_input}{prompt_suffix}{assistant_prompt}"
    image = Image.fromarray(image).convert("RGB")

    inputs = processor(prompt, image, return_tensors="pt")  # Varsayılan olarak CPU kullanılır
    generate_ids = model.generate(
        **inputs,
        max_new_tokens=2048,
        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 {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

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