import gradio as gr
import spaces
import torch
import os
import subprocess

# os.system("pip install git+https://github.com/huggingface/transformers")

from PIL import Image 
import requests 
from transformers import AutoModelForCausalLM 
from transformers import AutoProcessor 

model_id = "microsoft/Phi-3-vision-128k-instruct" 

model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype="auto", _attn_implementation='eager').cuda() # use _attn_implementation='eager' to disable flash attention

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) 

@spaces.GPU
def infer(u, t):
    if len(u) < 1:
        u = "https://lf3-static.bytednsdoc.com/obj/eden-cn/pbovhozuha/output.png"
    if len(t) < 1:
        t = "Convert the text in the image to markdown"
    messages = messages = [ 
        {"role": "user", "content": "<|image_1|>\n" + t},
    ] 
    url = u
    image = Image.open(requests.get(url, stream=True).raw) 

    prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

    inputs = processor(prompt, [image], return_tensors="pt").to(model.device)

    generation_args = { 
        "max_new_tokens": 512, 
        "temperature": 0.7, 
        "do_sample": True, 
    } 

    generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args) 

    # remove input tokens 
    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
    

demo = gr.Interface(
    fn=infer,
    inputs=[
        gr.Text(label="url"),
        gr.Text(label="text"),
    ],
    outputs=gr.Text(),
)
demo.launch()