Upload app.py

app.py

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+import os
+import json
+import torch
+from PIL import Image
+from torchvision import transforms
+import matplotlib.pyplot as plt
+import gradio as gr
+from io import BytesIO
+
+from vit_model import vit_base_patch16_224_in21k as create_model
+
+def classify_image(img):
+    # Your existing code here, modified to use `img_path` as input
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ 
+    data_transform = transforms.Compose(
+        [transforms.Resize(256),
+         transforms.CenterCrop(224),
+         transforms.ToTensor(),
+         transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+
+    # [N, C, H, W]
+    img = data_transform(img)
+    # expand batch dimension
+    img = torch.unsqueeze(img, dim=0)
+ 
+    # read class_indict
+    json_path = 'F:\mushroom_project\VIT\class_indices.json'
+    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)
+ 
+    with open(json_path, "r") as f:
+        class_indict = json.load(f)
+ 
+    # create model
+    model = create_model(num_classes=370, has_logits=False).to(device)
+    # load model weights
+    model_weight_path = "F:\mushroom_project\VIT\pretrain_30_weights\\best_model.pth"
+    #load no pretrain model path
+    #model_weight_path = "F:\mushroom_project\VIT\no_pretrain_weights\best_model.pth"
+    model.load_state_dict(torch.load(model_weight_path, map_location=device))
+    model.eval()
+    with torch.no_grad():
+        # predict class
+        output = torch.squeeze(model(img.to(device))).cpu()
+        predict = torch.softmax(output, dim=0)
+        predict_cla = torch.argmax(predict).numpy()
+ 
+    print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],
+                                                 predict[predict_cla].numpy())
+
+    # Combine the two lists into a list of tuples
+    combined_list = list(zip(class_indict, predict))
+
+    # Sort the combined list by the 'predict' values in descending order
+    sorted_combined_list = sorted(combined_list, key=lambda x: x[1], reverse=True)
+
+    # Determine the position you are currently interested in
+    current_position = 5  # Example position
+
+    # Get the previous five elements from the sorted list
+    # Ensure that the index does not go below zero
+    start_index = max(current_position - 5, 0)
+    previous_five = sorted_combined_list[start_index:current_position]
+
+    joined_string = ""
+    for i in previous_five:
+        #print("class: {:10}   prob: {:.3}".format(class_indict[str(i[0])], i[1].numpy()))
+        joined_string += ("class: {:10}   prob: {:.3}".format(class_indict[str(i[0])], i[1].numpy())) + "\n"
+
+    #print(joined_string)    
+    plt.title(joined_string)
+    plt.tight_layout()
+    fig = plt.figure()
+    return joined_string
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image, 
+    inputs=gr.Image(type='pil'), 
+    outputs=gr.Textbox(),
+    title="Mushrrom Image Classification",
+    description="Upload a mushroom image to classify."
+)
+
+# Run the Gradio app
+#if __name__ == '__main__':
+iface.launch()