Add application file

app.py

@@ -0,0 +1,52 @@
+import streamlit as st
+import torch
+from torchvision import models, transforms
+from PIL import Image
+import json
+
+# Load pretrained ResNet50 model
+model = models.resnet50(pretrained=True)
+model.eval()
+
+# Image preprocessing
+def process_image(image):
+    transform = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(
+            mean=[0.485, 0.456, 0.406],
+            std=[0.229, 0.224, 0.225]
+        )
+    ])
+    return transform(image).unsqueeze(0)
+
+# Load ImageNet class labels
+with open('imagenet_classes.json', 'r') as f:
+    class_labels = json.load(f)
+
+# Streamlit UI
+st.title("Image Classification with ResNet50")
+st.write("Upload an image and the model will classify it!")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display uploaded image
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Image', use_container_width=True)
+    
+    # Make prediction
+    input_tensor = process_image(image)
+    
+    with torch.no_grad():
+        output = model(input_tensor)
+        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+        
+    # Get top 5 predictions
+    top5_prob, top5_idx = torch.topk(probabilities, 5)
+    
+    # Display results
+    st.write("Top 5 Predictions:")
+    for i in range(5):
+        st.write(f"{class_labels[str(top5_idx[i].item())]}: {top5_prob[i].item()*100:.2f}%")