Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+from cal import load_model, predict_image, calculate_calories
+
+# Load the model
+model = load_model()
+
+# Set up the sidebar
+st.sidebar.title("Green Food Calorie Detector")
+st.sidebar.write("Upload an image or use your camera to take a picture.")
+
+option = st.sidebar.selectbox(
+    'How would you like to provide the image?',
+    ('Upload an image', 'Use camera')
+)
+
+image_path = None
+if option == 'Upload an image':
+    uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "webp"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        if image.mode == 'RGBA':
+            image = image.convert('RGB')
+        image_path = "uploaded_image.jpg"
+        image.save(image_path)
+elif option == 'Use camera':
+    camera_image = st.sidebar.camera_input("Take a picture")
+    if camera_image is not None:
+        image = Image.open(camera_image)
+        if image.mode == 'RGBA':
+            image = image.convert('RGB')
+        image_path = "camera_image.jpg"
+        image.save(image_path)
+
+if image_path:
+    # Display the image and classification results in columns
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.image(image, caption='Captured Image.', use_column_width=True)
+        st.write("")
+        st.write("Classifying...")
+
+    # Predict the image
+    image_with_boxes, detection_details = predict_image(image_path, model)
+
+    with col2:
+        # Display the image with bounding boxes and labels
+        st.image(image_with_boxes, caption='Processed Image.', use_column_width=True)
+
+    # Calculate and display detected items and their calories
+    detected_items = calculate_calories(detection_details)
+    st.markdown("<h3>Detection Results:</h3>", unsafe_allow_html=True)
+    for item, calories, confidence in detected_items:
+        st.markdown(f"<p style='font-size:18px;'>✓ Detected {item} ({calories} cal/100g) - Confidence: {confidence:.2%}</p>", unsafe_allow_html=True)
+
+# Footer
+st.markdown("""
+    <style>
+        .footer {
+            position: fixed;
+            left: 0;
+            bottom: 0;
+            width: 100%;
+            background-color: #f1f1f1;
+            color: black;
+            text-align: center;
+            padding: 10px;
+        }
+    </style>
+    <div class="footer">
+        <p>Green Food Calorie Detector © 2023</p>
+    </div>
+""", unsafe_allow_html=True)