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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+back_image.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,159 @@
+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! 😊")
+
+

db.json

@@ -0,0 +1 @@
+{"food": [{"name": "milk", "nutrients": {"calories": 61.0, "protein": 3.15, "fat": 3.25, "carbs": 4.8, "fiber": 0.0}}, {"name": "yogurt", "nutrients": {"calories": 61.0, "protein": 3.47, "fat": 3.25, "carbs": 4.66, "fiber": 0.0}}, {"name": "rice", "nutrients": {"calories": 130.0, "protein": 2.69, "fat": 0.28, "carbs": 28.17, "fiber": 0.4}}, {"name": "mixed rice", "nutrients": {"calories": 130.0, "protein": 2.69, "fat": 0.28, "carbs": 28.17, "fiber": 0.4}}, {"name": "bread", "nutrients": {"calories": 267.0, "protein": 10.72, "fat": 3.24, "carbs": 48.68, "fiber": 4.0}}, {"name": "white bread", "nutrients": {"calories": 267.0, "protein": 10.72, "fat": 3.24, "carbs": 48.68, "fiber": 4.0}}, {"name": "udon", "nutrients": {"calories": 158.0, "protein": 5.8, "fat": 0.93, "carbs": 30.86, "fiber": 1.8}}, {"name": "fish", "nutrients": {"calories": 128.0, "protein": 26.15, "fat": 2.65, "carbs": 0.0, "fiber": 0.0}}, {"name": "meat", "nutrients": {"calories": 130.0, "protein": 21.64, "fat": 4.81, "carbs": 0.12, "fiber": 0.0}}, {"name": "salad", "nutrients": {"calories": 91.0, "protein": 6.824999999999999, "fat": 0.0, "carbs": 13.649999999999999, "fiber": 9.1}}, {"name": "cherry tomatoe", "nutrients": {"calories": 18.0, "protein": 0.88, "fat": 0.2, "carbs": 3.89, "fiber": 1.2}}, {"name": "soup", "nutrients": {"calories": 55.15751653332906, "protein": 2.545537426016032, "fat": 3.228792980719593, "carbs": 5.152632258583227, "fiber": 1.4802578510053779}}, {"name": "tofu", "nutrients": {"calories": 70.0, "protein": 8.19, "fat": 4.17, "carbs": 1.69, "fiber": 0.9}}, {"name": "bibimbap", "nutrients": {"calories": 134.0797801649617, "protein": 4.706840856019083, "fat": 4.57389179861034, "carbs": 18.950491692637346, "fiber": 1.0684204878066172}}, {"name": "fried noodles", "nutrients": {"calories": 225.27935723105878, "protein": 7.597034653249537, "fat": 9.428677379474825, "carbs": 26.522317889247855, "fiber": 1.242709176931137}}, {"name": "spaghetti", "nutrients": {"calories": 158.0, "protein": 5.8, "fat": 0.93, "carbs": 30.86, "fiber": 1.8}}, {"name": "citrus", "nutrients": {"calories": 97.0, "protein": 1.5, "fat": 0.2, "carbs": 25.0, "fiber": 10.6}}, {"name": "apple", "nutrients": {"calories": 52.0, "protein": 0.26, "fat": 0.17, "carbs": 13.81, "fiber": 2.4}}, {"name": "banh mi", "nutrients": {"calories": 229.55103005106932, "protein": 11.59282390879125, "fat": 4.793258714390808, "carbs": 34.681966709771025, "fiber": 1.5573945328518939}}, {"name": "banh xeo", "nutrients": {"calories": 360.7564309845959, "protein": 8.016809577435465, "fat": 1.0021011971794331, "carbs": 82.17229816871351, "fiber": 0.0}}, {"name": "bun bo hue", "nutrients": {"calories": 278.0, "protein": 9.87, "fat": 3.76, "carbs": 50.15, "fiber": 2.1}}, {"name": "bun dau", "nutrients": {"calories": 278.0, "protein": 9.87, "fat": 3.76, "carbs": 50.15, "fiber": 2.1}}, {"name": "com tam", "nutrients": {"calories": 433.0, "protein": 6.6, "fat": 14.2, "carbs": 72.7, "fiber": 3.4}}, {"name": "goi cuon", "nutrients": {"calories": 29.39496829131701, "protein": 0.0, "fat": 0.0, "carbs": 8.230591121568763, "fiber": 0.0}}, {"name": "pho", "nutrients": {"calories": 112.28533685601057, "protein": 4.227212681638045, "fat": 0.7926023778071335, "carbs": 22.324966974900928, "fiber": 0.7926023778071335}}, {"name": "pizza", "nutrients": {"calories": 268.0, "protein": 10.36, "fat": 12.28, "carbs": 29.02, "fiber": 2.2}}, {"name": "hamburger", "nutrients": {"calories": 153.39252215176927, "protein": 12.443173384785876, "fat": 6.132552442778505, "carbs": 11.398144276772499, "fiber": 1.1498570372985786}}, {"name": "french fries", "nutrients": {"calories": 150.0, "protein": 2.34, "fat": 4.99, "carbs": 23.96, "fiber": 2.0}}, {"name": "sandwich", "nutrients": {"calories": 212.35521235521236, "protein": 9.652509652509652, "fat": 10.424710424710424, "carbs": 20.077220077220076, "fiber": 1.1583011583011582}}, {"name": "cheese", "nutrients": {"calories": 406.0, "protein": 24.04, "fat": 33.82, "carbs": 1.33, "fiber": 0.0}}, {"name": "banana", "nutrients": {"calories": 89.0, "protein": 1.09, "fat": 0.33, "carbs": 22.84, "fiber": 2.6}}, {"name": "coffee", "nutrients": {"calories": 1.0, "protein": 0.12, "fat": 0.02, "carbs": 0.0, "fiber": 0.0}}, {"name": "tea", "nutrients": {"calories": 1.0, "protein": 0.0, "fat": 0.0, "carbs": 0.3, "fiber": 0.0}}, {"name": "cucumber", "nutrients": {"calories": 15.0, "protein": 0.65, "fat": 0.11, "carbs": 3.63, "fiber": 0.5}}, {"name": "egg", "nutrients": {"calories": 155.0, "protein": 12.58, "fat": 10.61, "carbs": 1.12, "fiber": 0.0}}, {"name": "orange", "nutrients": {"calories": 47.0, "protein": 0.94, "fat": 0.12, "carbs": 11.75, "fiber": 2.4}}, {"name": "nachos", "nutrients": {"calories": 257.4525745257452, "protein": 8.672086720867208, "fat": 15.176151761517614, "carbs": 21.95121951219512, "fiber": 2.9810298102981028}}, {"name": "mushroom risotto", "nutrients": {"calories": 111.85064049290168, "protein": 3.5518911811662264, "fat": 4.875915059118156, 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requirements (1).txt

@@ -0,0 +1,11 @@
+streamlit
+streamlit-option-menu
+torch
+torchvision
+albumentations
+opencv-python
+Pillow
+ultralytics
+openpyxl
+streamlit-lottie
+requests