Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Define the pipeline
+@st.cache_resource
+def load_pipeline():
+    return pipeline("image-classification", model="yangy50/garbage-classification")
+
+pipe = load_pipeline()
+
+# Streamlit UI
+st.title("Garbage Classification App")
+st.write("Upload an image to classify it as a type of garbage.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load image
+    image = Image.open(uploaded_file)
+    
+    # Display image
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Run inference
+    results = pipe(image)
+    
+    # Get top prediction
+    top_prediction = max(results, key=lambda x: x["score"])
+    
+    # Display result
+    st.write(f"**Predicted Class:** {top_prediction['label']}")
+    st.write(f"**Confidence:** {top_prediction['score']:.2f}")