app.py

import os
import sys
import argparse
import numpy as np

import cv2
import torch
import gradio as gr
from PIL import Image

# sys.path.insert(0, os.path.join(os.getcwd(), ".."))
# from unimernet.common.config import Config
# import unimernet.tasks as tasks
# from unimernet.processors import load_processor


# class ImageProcessor:
#     def __init__(self, cfg_path):
#         self.cfg_path = cfg_path
#         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#         self.model, self.vis_processor = self.load_model_and_processor()

#     def load_model_and_processor(self):
#         args = argparse.Namespace(cfg_path=self.cfg_path, options=None)
#         cfg = Config(args)
#         task = tasks.setup_task(cfg)
#         model = task.build_model(cfg).to(self.device)
#         vis_processor = load_processor('formula_image_eval', cfg.config.datasets.formula_rec_eval.vis_processor.eval)

#         return model, vis_processor

#     def process_single_image(self, image_path):
#         try:
#             raw_image = Image.open(image_path)
#         except IOError:
#             print(f"Error: Unable to open image at {image_path}")
#             return
#         # Convert PIL Image to OpenCV format
#         open_cv_image = np.array(raw_image)
#         # Convert RGB to BGR
#         if len(open_cv_image.shape) == 3:
#             # Convert RGB to BGR
#             open_cv_image = open_cv_image[:, :, ::-1].copy()
#         # Display the image using cv2

#         image = self.vis_processor(raw_image).unsqueeze(0).to(self.device)
#         output = self.model.generate({"image": image})
#         pred = output["pred_str"][0]
#         print(f'Prediction:\n{pred}')

#         cv2.imshow('Original Image', open_cv_image)
#         cv2.waitKey(0)
#         cv2.destroyAllWindows()

#         return pred
    

def recognize_image(input_img):
    # latex_code = processor.process_single_image(input_img.name)
    return "100"
    
def gradio_reset():
    return gr.update(value=None), gr.update(value=None)

    
if __name__ == "__main__":
    # == init model ==
    # root_path = os.path.abspath(os.getcwd())
    # config_path = os.path.join(root_path, "cfg_tiny.yaml")

    # processor_tiny = ImageProcessor(config_path)
    # print("== all models init. ==")
    # == init model ==
    
    with open("header.html", "r") as file:
        header = file.read()
    with gr.Blocks() as demo:
        gr.HTML(header)
        
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label=" ", interactive=True)
                with gr.Row():
                    clear = gr.Button("Clear")
                    predict = gr.Button(value="Recognize", interactive=True, variant="primary")
            with gr.Column():
                gr.Button(value="Predict Latex:", interactive=False)
                pred_latex = gr.Textbox(label='Latex', interactive=False)
    
        clear.click(gradio_reset, inputs=None, outputs=[input_img, pred_latex])
        predict.click(recognize_image, inputs=[input_img], outputs=[pred_latex])
    
    demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)