Added streamlit classifier widget

app.py

@@ -1,7 +1,6 @@
 import timm
 import torch
-import requests
-from PIL import Image
+import streamlit as st
 from timm.data import resolve_data_config
 from timm.data.transforms_factory import create_transform
 
@@ -10,25 +9,20 @@ model = timm.create_model(
     'hf-hub:nateraw/resnet50-oxford-iiit-pet',
     pretrained=True
 )
-
 model.eval()
-
 transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))
-
 labels = model.pretrained_cfg['label_names']
-
-url = 'https://datasets-server.huggingface.co/assets/imagenet-1k/--/default/test/12/image/image.jpg'
-image = Image.open(requests.get(url, stream=True).raw)
-
-output = model(transform(image).unsqueeze(0))
-
-probabilities = torch.nn.functional.softmax(output[0], dim=0)
-
-values, indices = torch.topk(probabilities, 5)
-
-predictions = [
-    {'label': labels[i], 'score': v.item()}
-    for i, v in zip(indices, values)
-]
-
-print(predictions)
+st.title("Pet Image Classification App")
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+if uploaded_image is not None:
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+    st.subheader("Classification Results:")
+    output = model(transform(uploaded_image).unsqueeze(0))
+    probabilities = torch.nn.functional.softmax(output[0], dim=0)
+    values, indices = torch.topk(probabilities, 5)
+    predictions = [
+        {'label': labels[i], 'score': v.item()}
+        for i, v in zip(indices, values)
+    ]
+    for prediction in predictions:
+        st.write(f"- {prediction.label}: {prediction.score:.4f}")