Create app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from PIL import Image
+
+# Load the model
+loaded_model = models.densenet121()
+
+num_features = loaded_model.classifier.in_features
+loaded_model.classifier = nn.Linear(num_features, 5)
+loaded_model.load_state_dict(torch.load('derma_diseases_detection_best.pt', map_location=torch.device('cpu')))
+loaded_model.eval()
+
+# Define the image preprocessing function
+def preprocess_image(image):
+    image = Image.fromarray(image)
+    # Transform the image using the same transformations as during training
+    transform = transforms.Compose([
+        transforms.Resize([224, 224]),
+        transforms.ToTensor(),
+        #transforms.Normalize(mean=[0.5523, 0.5288, 0.5106], std=[0.1012, 0.0820, 0.0509])
+    ])
+    image = transform(image)
+    image = image.unsqueeze(0)  # Add batch dimension
+    return image
+
+# Define the prediction function
+def predict_skin_disease(image):
+    # Preprocess the input image
+    preprocessed_image = preprocess_image(image)
+
+    # Make prediction
+    with torch.no_grad():
+        output = loaded_model(preprocessed_image)
+        _, predicted_class = torch.max(output, 1)
+
+    # Map the predicted class index to the corresponding class label
+    class_label = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative']
+    class_label = class_label[predicted_class.item()]
+
+    return class_label
+
+# Streamlit app
+st.title("Skin Disease Detection")
+
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Display the uploaded image
+    st.image(uploaded_image, caption="Uploaded Image.", use_column_width=True)
+
+    # Convert the image to the format expected by the model
+    image = Image.open(uploaded_image)
+    input_image = preprocess_image(image)
+
+    # Make prediction
+    prediction = predict_skin_disease(input_image)
+
+    # Display the prediction
+    st.success(f"Prediction: {prediction}")