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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 | |
| 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! 😊") | |