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import streamlit as st
import os
import json
import cv2
import pandas as pd
from ultralytics import YOLO
from PIL import Image
# Configure Streamlit pages
st.set_page_config(
page_title="NutriDetect: AI-powered Food Detection and Nutritional Insights",
page_icon="🍴",
# Set layout to wide for better visualization
)
# Sidebar navigation
st.sidebar.title("Navigation")
page = st.sidebar.radio("Go to", ["Home", "Description", "Try Models"])
# Load nutritional data from the JSON file
def load_nutritional_data(json_path):
with open(json_path, 'r') as file:
data = json.load(file)
for food in data["food"]:
food["preprocessed_name"] = food["name"].lower().replace(",", "").strip()
return data
# Load models
@st.cache_resource
def load_models():
yolov8_model = YOLO("./model/Yolov8m.pt")
yolov11_model = YOLO("./model/Yolo11m.pt")
return yolov8_model, yolov11_model
# Global variables for models and nutritional data
models = load_models()
nutritional_json_path = "db.json"
nutritional_data = load_nutritional_data(nutritional_json_path)
# Home Page
if page == "Home":
st.title("🍴 NutriDetect: Food Detection and Nutritional Insights")
st.markdown("**Welcome to the NutriDetect App!** Leverage cutting-edge AI to identify food items and provide nutritional details.")
st.image("back_image.png", use_container_width=True, caption="AI-powered Food Detection")
st.subheader("🌟 Key Features")
st.markdown("""
- **Accurate Food Detection**: Identify food items in images.
- **Nutritional Insights**: Get calorie, protein, fat, and other details for detected food.
""")
# Description Page
elif page == "Description":
st.title("📖 About the NutriDetect App")
st.markdown("""
The NutriDetect App uses YOLO-based models for accurate food detection and provides:
- Bounding boxes for detected food items.
- Detailed nutritional insights for the detected items.
""")
# Try Models Page
elif page == "Try Models":
st.title("🎯 Try the Models")
uploaded_file = st.file_uploader("Upload an image (jpg, jpeg, png)", type=["jpg", "jpeg", "png"])
if uploaded_file:
st.subheader("Step 2: Choose Model")
model_option = st.selectbox("Select Model", ["YOLOv8m", "YOLO11m"])
if st.button("🔍 Detect"):
with st.spinner("Processing..."):
# Save the uploaded file temporarily
temp_file_path = "temp_image.jpg"
with open(temp_file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
# Perform detection
model = models[0] if model_option == "YOLOv8m" else models[1]
results = model(temp_file_path)
# Process detected items
detected_items = []
for box in results[0].boxes.data.cpu().numpy():
label = int(box[5]) # Get the class label
class_name = model.names[label]
nutrition_info = next(
(food["nutrients"] for food in nutritional_data["food"] if food["preprocessed_name"] == class_name.lower().replace(",", "").strip()),
None
)
if nutrition_info:
detected_items.append(
{
"Food": class_name,
"Calories": round(nutrition_info.get("calories", 0), 2),
"Protein": round(nutrition_info.get("protein", 0), 2),
"Fat": round(nutrition_info.get("fat", 0), 2),
"Carbs": round(nutrition_info.get("carbs", 0), 2),
"Fiber": round(nutrition_info.get("fiber", 0), 2),
}
)
# Handle case with no detected items
if not detected_items:
st.warning("No food items detected, sorry!")
else:
# Convert detected items to DataFrame
df = pd.DataFrame(detected_items)
# Calculate totals
totals = {
"Calories": round(df["Calories"].sum(), 2),
"Protein": round(df["Protein"].sum(), 2),
"Fat": round(df["Fat"].sum(), 2),
"Carbs": round(df["Carbs"].sum(), 2),
"Fiber": round(df["Fiber"].sum(), 2),
}
# Display annotated image
annotated_image = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
st.image(annotated_image, caption=f"{model_option} Results", use_container_width=True)
# Two-column layout for detected items and totals
left_col, right_col = st.columns([2, 1])
# Left column: Individual food items
with left_col:
st.header("Detected Food Items")
for _, row in df.iterrows():
st.subheader(row["Food"])
st.write(f"Calories: {row['Calories']} Cal")
st.write(f"Protein: {row['Protein']} g")
st.write(f"Fat: {row['Fat']} g")
st.write(f"Carbs: {row['Carbs']} g")
st.write(f"Fiber: {row['Fiber']} g")
st.write("---")
# Right column: Total nutritional summary
# Right column: Total nutritional summary
# Right column: Total nutritional summary
with right_col:
st.header("Total Nutritional Summary")
st.metric("Total Calories", f"{totals['Calories']} Cal")
st.metric("Total Protein", f"{totals['Protein']} g")
st.metric("Total Fat", f"{totals['Fat']} g")
st.metric("Total Carbs", f"{totals['Carbs']} g")
st.metric("Total Fiber", f"{totals['Fiber']} g")
# Check for unhealthy items and provide recommendations
detected_foods = df["Food"].str.lower().tolist() # Convert to lowercase for matching
unhealthy_items = [item for item in detected_foods if item in ["pizza", "hamburger", "french fries"]]
if unhealthy_items:
st.warning(
f"Detected unhealthy food items: {', '.join(unhealthy_items)}. "
"Consider replacing them with fruits and green salad for better health. 🥗🍎"
)
else:
st.success("Balanced meals lead to a healthier life! 😊")
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