Create app.py

app.py

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+import torch
+import torchvision.transforms as transforms
+import torchvision.models as models
+from joblib import load
+from PIL import Image
+import gradio as gr
+import matplotlib.pyplot as plt
+import io
+
+# Transformation and device setup
+device = torch.device("cpu")
+data_transforms = transforms.Compose([
+    transforms.Resize(224),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+# Load the Isolation Forest model
+clf = load('Models/Anomaly_MSI_MSS_Isolation_Forest_model.joblib')
+
+# Load feature extractor
+feature_extractor_path = 'Models/feature_extractor.pth'
+feature_extractor = models.resnet50(weights=None)
+feature_extractor.fc = nn.Sequential()
+feature_extractor.load_state_dict(torch.load(feature_extractor_path, map_location=device))
+feature_extractor.to(device)
+feature_extractor.eval()
+
+# Load gastric classification model
+GASTRIC_MODEL_PATH = 'Gastric_Models/the_resnet_50_model.pth'
+model_ft = torch.load(GASTRIC_MODEL_PATH, map_location=device)
+model_ft.to(device)
+model_ft.eval()
+
+# Anomaly detection and classification function
+def classify_image(uploaded_image):
+    image = Image.open(io.BytesIO(uploaded_image.read())).convert('RGB')
+    input_image = data_transforms(image).unsqueeze(0).to(device)
+
+    # Anomaly detection
+    if is_anomaly(clf, feature_extractor):
+        return "Anomaly detected. Image will not be classified.", None
+
+    # Classification
+    with torch.no_grad():
+        outputs = model_ft(input_image)
+        probabilities = torch.nn.functional.softmax(outputs, dim=1)
+        _, predicted = torch.max(outputs, 1)
+
+    predicted_class_index = predicted.item()
+    class_names = ['abnormal', 'normal']
+    predicted_class_name = class_names[predicted_class_index]
+    predicted_probability = probabilities[0][predicted_class_index].item() * 100
+
+    return f"Class: {predicted_class_name}, Probability: {predicted_probability:.2f}%", None
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=File(type="filepath"),
+    [gr.outputs.Text(), gr.outputs.Image(plot=True)],
+    title="Gastric Image Classifier",
+    description="Upload a gastric image to classify it as normal or abnormal."
+)
+
+# Run the Gradio app
+iface.launch()