Create app.py

app.py

@@ -0,0 +1,26 @@
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# Load a food classification pipeline
+food_pipeline = pipeline(task="image-classification", model="microsoft/resnet-50")
+
+st.title("Food Recognition Agent 🍕")
+
+# Upload the image
+file_name = st.file_uploader("Upload a food image")
+
+if file_name is not None:
+    col1, col2 = st.columns(2)
+
+    # Display the uploaded image
+    image = Image.open(file_name)
+    col1.image(image, use_container_width=True, caption="Uploaded Image")
+    
+    # Make predictions
+    predictions = food_pipeline(image)
+
+    # Display probabilities
+    col2.header("Food Predictions 🍽️")
+    for p in predictions:
+        col2.subheader(f"{p['label']}: {round(p['score'] * 100, 1)}%")