Create app.py

app.py

@@ -0,0 +1,36 @@
+import streamlit as st
+from transformers import pipeline, AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import requests
+
+# Load the model and processor
+st.title("Food Image Classification with Hugging Face")
+st.write("Upload an image to classify the type of food!")
+
+# Load the model
+@st.cache_resource
+def load_pipeline():
+    return pipeline("image-classification", model="Shresthadev403/food-image-classification")
+
+pipe = load_pipeline()
+
+# Upload image
+uploaded_file = st.file_uploader("Choose a food image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    st.write("Classifying...")
+    
+    # Make predictions
+    predictions = pipe(image)
+    
+    # Display top prediction
+    st.subheader("Top Prediction")
+    st.write(f"**{predictions[0]['label']}** with confidence {predictions[0]['score']:.2f}")
+    
+    # Display other predictions
+    st.subheader("Other Predictions")
+    for pred in predictions[1:]:
+        st.write(f"{pred['label']}: {pred['score']:.2f}")