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)