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README.md

@@ -4,7 +4,7 @@ emoji: 👁
 colorFrom: purple
 colorTo: blue
 sdk: gradio
-sdk_version: 4.16.0
+sdk_version: 4.43.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -1,6 +1,6 @@
-import argparse
 import os
 import sys
+import argparse
 import numpy as np
 
 import cv2
@@ -8,51 +8,51 @@ 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
+# 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()
+# 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)
+#     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
+#         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
+#     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}')
+#         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()
+#         cv2.imshow('Original Image', open_cv_image)
+#         cv2.waitKey(0)
+#         cv2.destroyAllWindows()
 
-        return pred
+#         return pred
     
 
 def recognize_image(input_img):

requirements.txt

@@ -1,2 +1,2 @@
-unimernet==0.2.0
-gradio==4.16.0
+# unimernet==0.2.0
+gradio==4.43.0