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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) |