Synced repo using 'sync_with_huggingface' Github Action

gradio_app.py

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+
+import gradio as gr
+
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+import os
+import onnxruntime
+import numpy as np
+
+def predict_fault(image, model):
+    image = image.detach().cpu().numpy()
+    input = {model.get_inputs()[0].name: image}
+    output = model.run(None, input)
+    preds = np.argmax(output[0], 1)
+    return preds.item()
+
+def detect(image, writing_type, post_it, corner, empty):
+    writing_type_model, post_it_model, corner_model, empty_model = models
+    
+    res_dict = {}
+
+    if writing_type:
+        input_image = writing_type_transforms(image).unsqueeze(0)
+        label = predict_fault(input_image, writing_type_model)
+        res_dict['writing_type'] = label
+
+    if post_it:
+        input_image = data_transforms(image).unsqueeze(0)
+        label = predict_fault(input_image, post_it_model)
+        res_dict['post_it'] = label
+
+    if corner:
+        input_image = data_transforms(image).unsqueeze(0)
+        label = predict_fault(input_image, corner_model)
+        res_dict['corner'] = label
+
+    if empty:
+        input_image = empty_transforms(image).unsqueeze(0)
+        label = predict_fault(input_image, empty_model)
+        res_dict['empty'] = 1 - label
+
+    return res_dict
+
+def load_models():
+    try:
+        MODEL_PATH = os.environ.get("MODEL_PATH", './models/')
+        POST_IT_MODEL = os.environ.get("POST_IT_MODEL", 'post_it_model.onnx')
+        CORNER_MODEL = os.environ.get("CORNER_MODEL", 'corner_model.onnx')
+        EMPTY_MODEL = os.environ.get("EMPTY_MODEL", 'empty_v5_24_08_23.onnx')
+        WRITING_TYPE_MODEL = os.environ.get("WRITING_TYPE_MODEL", 'writing_type_v1.onnx')
+
+        print(f"ORT device: {onnxruntime.get_device()}")
+
+        # Load the models and the trained weights
+        writing_type_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, WRITING_TYPE_MODEL))
+        post_it_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, POST_IT_MODEL))
+        corner_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, CORNER_MODEL))
+        empty_model = onnxruntime.InferenceSession(os.path.join(MODEL_PATH, EMPTY_MODEL))
+
+        return writing_type_model, post_it_model, corner_model, empty_model
+    except Exception as e:
+        print("Failed to load pretrained models: {}".format(e))
+
+# Load the models
+models = load_models()
+
+# Transform methods for corner & post-it model inputs
+data_transforms = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor()
+    ])
+
+# Transform methods for empty model inputs
+empty_transforms = transforms.Compose([
+        transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+
+# Transform methods for writing-type model inputs
+writing_type_transforms = transforms.Compose([
+        transforms.Resize((224, 224), interpolation=InterpolationMode.BICUBIC),
+        transforms.ToTensor(),
+        transforms.Normalize([0.882, 0.883, 0.899], [0.088, 0.089, 0.094])
+    ])
+
+
+with gr.Blocks(title="Image Faulty Demo") as demo:
+    gr.Markdown("""
+    # Image Faulty
+    Find the project [here](https://github.com/xiaoyao9184/image-faulty).
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            detecting_img = gr.Image(label="Input Image", type="pil", height=512)
+        with gr.Column():
+            writing_ckb = gr.Checkbox(label="Writing type", value=True)
+            postit_ckb = gr.Checkbox(label="Post it", value=True)
+            corner_ckb = gr.Checkbox(label="Folded corner", value=True)
+            empty_ckb = gr.Checkbox(label="Parper empty", value=True)
+            detecting_btn = gr.Button("Detect")
+            predicted_messages = gr.JSON(label="Detected Messages")
+
+    detecting_btn.click(
+        fn=detect,
+        inputs=[detecting_img, writing_ckb, postit_ckb, corner_ckb, empty_ckb],
+        outputs=[predicted_messages]
+    )
+
+if __name__ == '__main__':
+    demo.launch()

requirements.txt

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+gradio==5.8.0
+onnxruntime==1.20.1
+torchvision==0.13.0
+numpy==1.21.6