Create app.py

app.py

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+import streamlit as st
+import torch
+from model import ToxicImageClassifier
+from utils import load_image, predict_toxicity, get_label
+from PIL import Image
+
+def main():
+    st.title("Toxic Image Classifier")
+    st.write("Upload an image to check if it contains toxic content")
+    
+    # Load model
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = ToxicImageClassifier()
+    model.load_state_dict(torch.load("toxic_classifier.pth", map_location=device))
+    model.to(device)
+    
+    # File uploader
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+    
+    if uploaded_file is not None:
+        # Display image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        
+        # Process and predict
+        with st.spinner("Analyzing..."):
+            image_tensor = load_image(uploaded_file)
+            prediction, probabilities = predict_toxicity(model, image_tensor, device)
+            label = get_label(prediction)
+            
+            # Display results
+            st.write(f"Prediction: **{label}**")
+            st.write(f"Confidence: Toxic: {probabilities[1]:.2%}, Non-Toxic: {probabilities[0]:.2%}")
+            
+            # Probability bar chart
+            st.bar_chart({"Toxic": probabilities[1], "Non-Toxic": probabilities[0]})
+
+if __name__ == "__main__":
+    main()