Update app.py

app.py

@@ -1,45 +1,51 @@
-from flask import Flask, request, jsonify
+import streamlit as st
 from transformers import pipeline
 from PIL import Image
 import io
 
-app = Flask(__name__)
+# Set Streamlit page config
+st.set_page_config(page_title="Food Image Classifier", layout="centered")
 
-# Load model at startup
-print("Loading model...")
-classifier = pipeline("image-classification", model="Xenova/mobilenet_v2_1.0_224")
-print("Model loaded successfully!")
+# Load the model
+@st.cache_resource
+def load_model():
+    st.text("Loading model...")
+    model = pipeline("image-classification", model="Xenova/mobilenet_v2_1.0_224")
+    st.text("Model loaded successfully!")
+    return model
 
-@app.route('/')
-def home():
-    return "Food Image Classifier API is running!"
+classifier = load_model()
 
-@app.route('/classify', methods=['POST'])
-def classify_image():
-    if 'file' not in request.files:
-        return jsonify({"error": "No file provided"}), 400
+# Streamlit UI
+st.title("🍕🥖 Food Image Classifier")
+st.write("Upload an image of **roti, pizza, naan, or tofu** to classify.")
 
-    file = request.files['file']
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-    try:
-        # Convert file to PIL image
-        image = Image.open(io.BytesIO(file.read()))
+if uploaded_file is not None:
+    # Convert file to PIL image
+    image = Image.open(uploaded_file)
 
-        # Run classification
+    # Display the uploaded image
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Classify the image
+    with st.spinner("Classifying..."):
         results = classifier(image)
 
-        # Get top result
-        if results:
-            prediction = {
-                "label": results[0]['label'],
-                "confidence": results[0]['score']
-            }
-            return jsonify(prediction)
-        else:
-            return jsonify({"error": "No results returned"}), 500
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(host="0.0.0.0", port=7860, debug=True)
+    # Display results
+    if results:
+        label = results[0]['label']
+        confidence = results[0]['score'] * 100  # Convert to percentage
+
+        st.success(f"**Prediction:** {label}")
+        st.info(f"**Confidence:** {confidence:.2f}%")
+
+        # Option to classify another image
+        st.button("Classify Another Image", on_click=lambda: st.experimental_rerun())
+
+# Footer
+st.markdown("---")
+st.markdown("Made by **Muneeb Sahaf** | Final Year Project 2025")
+
+