From 3dfa98e1434e75bed40825c4334ea6d7bedffce1 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Wed, 25 Jun 2025 11:27:14 +0530
Subject: [PATCH 01/13] feat: Implement image upload and ml model

---
 __pycache__/script.cpython-310.pyc         | Bin 0 -> 789 bytes
 ml_model/__pycache__/model.cpython-310.pyc | Bin 0 -> 1730 bytes
 ml_model/model.py                          |  46 +++++++++++++++++++++
 requirements.txt                           |   4 ++
 script.py                                  |  22 ++++++++++
 5 files changed, 72 insertions(+)
 create mode 100644 __pycache__/script.cpython-310.pyc
 create mode 100644 ml_model/__pycache__/model.cpython-310.pyc
 create mode 100644 ml_model/model.py
 create mode 100644 requirements.txt
 create mode 100644 script.py

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diff --git a/ml_model/model.py b/ml_model/model.py
new file mode 100644
index 00000000..e37889eb
--- /dev/null
+++ b/ml_model/model.py
@@ -0,0 +1,46 @@
+import tensorflow as tf 
+import cv2 as cv
+import numpy as np
+
+
+def food_ai_model(image):
+    model = tf.keras.models.load_model('/home/acer/Downloads/indian_food_model.keras')
+    # model.summary()
+
+    # image=cv.imread(image, cv.IMREAD_COLOR_RGB)
+    img = cv.imdecode(np.frombuffer(image, dtype= np.uint8), cv.IMREAD_COLOR_RGB)    
+    image_resized = cv.resize(img, (180,180))
+    image = np.expand_dims(image_resized,axis=0)
+    # print(image.shape)
+
+    pred = model.predict(image)
+    # print(pred)
+
+    class_names = ['adhirasam', 'aloo_gobi', 'aloo_matar', 'aloo_methi', 'aloo_shimla_mirch', 'aloo_tikki', 'anarsa', 'ariselu', 
+                   'bandar_laddu', 'basundi', 'bhatura', 'bhindi_masala', 'biryani', 'boondi', 'butter_chicken', 'chak_hao_kheer', 
+                   'cham_cham', 'chana_masala', 'chapati', 'chhena_kheeri', 'chicken_razala', 'chicken_tikka', 'chicken_tikka_masala', 
+                   'chikki', 'daal_baati_churma', 'daal_puri', 'dal_makhani', 'dal_tadka', 'dharwad_pedha', 'doodhpak', 'dosa', 
+                   'double_ka_meetha', 'dum_aloo', 'gajar_ka_halwa', 'gavvalu', 'ghee_rice', 'ghevar', 'grilled_chicken', 'gulab_jamun', 
+                   'idli', 'imarti', 'jalebi', 'kachori', 'kadai_paneer', 'kadhi_pakoda', 'kajjikaya', 'kakinada_khaja', 'kalakand', 
+                   'karela_bharta', 'kofta', 'kuzhi_paniyaram', 'lassi', 'ledikeni', 'litti_chokha', 'lyangcha', 'maach_jhol', 
+                   'makki_di_roti_sarson_da_saag', 'malapua', 'misi_roti', 'misti_doi', 'modak', 'mysore_pak', 'naan', 'navrattan_korma', 
+                   'palak_paneer', 'paneer_butter_masala', 'phirni', 'pithe', 'poha', 'poornalu', 'pootharekulu', 'puttu', 'qubani_ka_meetha', 
+                   'rabri', 'ras_malai', 'rasgulla', 'rice', 'sandesh', 'shankarpali', 'sheer_korma', 'sheera', 'shrikhand', 'sohan_halwa', 
+                   'sohan_papdi', 'sutar_feni', 'unni_appam']
+
+    output_class = class_names[np.argmax(pred)]
+    # print("The predicted class is", output_class)
+    return output_class
+     
+
+
+# image = '/home/acer/Downloads/archive/Indian Food Images/idli/fbbfimages.jpeg'
+
+# food_ai_model(image)
+
+
+
+
+
+
+
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 00000000..c71383d0
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+fastapi==0.115.13
+uvicorn==0.34.3
+tensorflow==2.19.0
+opencv-python==4.11.0.86
\ No newline at end of file
diff --git a/script.py b/script.py
new file mode 100644
index 00000000..29bc1e31
--- /dev/null
+++ b/script.py
@@ -0,0 +1,22 @@
+from fastapi import FastAPI, UploadFile
+from starlette import status
+from ml_model.model import food_ai_model
+
+app = FastAPI()
+
+@app.get('/')
+async def project():
+
+    return ({"message": "Food snap Project"})
+
+
+@app.post('/foodsnap/v1/upload', status_code= status.HTTP_200_OK)
+async def fetch_data(image: UploadFile, quantity: int):
+
+    uploaded_image = await image.read()
+    food_name = food_ai_model(uploaded_image)
+    food_quantity = quantity
+
+    return ({"name": food_name, "quantity": food_quantity})
+
+

From d6a3db63fb8710897bd5a596736fb6e5f5962528 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Mon, 14 Jul 2025 19:00:58 +0530
Subject: [PATCH 02/13] feat: Implement application lifespan for single model
 loading and Add dynamic nutrinal value calculation

---
 README.md                                  | 193 +--------------------
 __pycache__/database.cpython-311.pyc       | Bin 0 -> 648 bytes
 __pycache__/models.cpython-311.pyc         | Bin 0 -> 1370 bytes
 __pycache__/script.cpython-311.pyc         | Bin 0 -> 4064 bytes
 data-processing.py                         |  50 ++++++
 database.py                                |  12 ++
 ml_model/__pycache__/model.cpython-311.pyc | Bin 0 -> 2437 bytes
 ml_model/model.py                          |  61 ++++---
 models.py                                  |  21 +++
 requirements.txt                           |   5 +-
 script.py                                  |  68 +++++++-
 11 files changed, 181 insertions(+), 229 deletions(-)
 create mode 100644 __pycache__/database.cpython-311.pyc
 create mode 100644 __pycache__/models.cpython-311.pyc
 create mode 100644 __pycache__/script.cpython-311.pyc
 create mode 100644 data-processing.py
 create mode 100644 database.py
 create mode 100644 ml_model/__pycache__/model.cpython-311.pyc
 create mode 100644 models.py

diff --git a/README.md b/README.md
index b58f55ca..d591f8f3 100644
--- a/README.md
+++ b/README.md
@@ -1,194 +1,3 @@
-# FoodSnap AI - Hackathon Project
+# FoodSnap AI - Project
 
-🎉 **Welcome to the FoodSnap AI Hackathon!** 🎉
 
-Get ready to combine your coding skills with the fascinating world of artificial intelligence and nutrition! We're excited to see what innovative solutions you come up with.
-
-**The Challenge:**
-Your mission, should you choose to accept it, is to develop a solution that can estimate the caloric content (and ideally, macronutrient breakdown – protein, carbs, fats) of a food item from an input image.
-
-## Table of Contents
-
-1.  [Project Overview](#project-overview)
-2.  [Why This Project?](#why-this-project)
-3.  [Scope & Implementation Freedom](#project-scope--implementation-freedom)
-4.  [Key Objectives / Potential Features](#key-objectives--potential-features)
-5.  [Helpful Resources & APIs](#helpful-resources--apis)
-    *   [Food Image Datasets](#food-image-datasets)
-    *   [Nutrition Information Databases & APIs](#nutrition-information-databases--apis)
-    *   [Machine Learning / AI Tools](#machine-learning--ai-tools)
-6.  [Getting Started: Git & Submission Workflow](#getting-started-git--submission-workflow)
-    *   [1. Fork the Repository](#1-fork-the-repository)
-    *   [2. Clone Your Forked Repository](#2-clone-your-forked-repository)
-    *   [3. Create a New Branch](#3-create-a-new-branch)
-    *   [4. Develop Your Project](#4-develop-your-project)
-    *   [5. Push Your Branch to Your Fork](#5-push-your-branch-to-your-fork)
-    *   [6. Submit Your Project (Create a Pull Request)](#6-submit-your-project-create-a-pull-request)
-7.  [What to Include in Your Submission](#what-to-include-in-your-submission)
-8.  [Judging Criteria (General Pointers)](#judging-criteria-general-pointers)
-
-
----
-
-## Project Overview
-
-**Project Name:** FoodSnap AI
-
-**The Challenge:**
-Develop a solution that can estimate the caloric content (and ideally, macronutrient breakdown – protein, carbs, fats) of a food item from an input image.
-
-## Why This Project?
-
-Understanding calorie intake is crucial for health and fitness. Manually logging food can be tedious. FoodSnap AI aims to simplify this process, making nutritional awareness more accessible to everyone. Imagine snapping a photo of your meal and instantly getting its nutritional information!
-
-## Project Scope & Implementation Freedom
-
-You have complete freedom in how you bring FoodSnap AI to life!
-
-*   **Platform:** Mobile App (iOS, Android, cross-platform), Web App, Website, or even a Command Line Interface (CLI) tool.
-*   **Technology Stack:** Use any programming languages, frameworks, libraries, or APIs you prefer. Python, JavaScript, Java, Swift, Kotlin, Ruby, Go – the choice is yours!
-*   **Approach:** You can use pre-trained machine learning models, train your own, leverage existing food recognition APIs, or come up with a completely novel approach.
-
-## Key Objectives / Potential Features
-
-1.  **Image Input:** The system must accept an image of food as input.
-2.  **Food Identification (Implicit or Explicit):** The system needs to identify the food item(s) in the image. This could be a direct output or an internal step.
-3.  **Calorie Estimation:** Based on the identified food, estimate its total calories.
-4.  **Macronutrient Breakdown (Bonus):** If possible, also estimate protein, carbohydrates, and fats.
-5.  **Portion Size Consideration (Advanced Bonus):** Accurately estimating portion size from an image is challenging but would be a significant enhancement.
-6.  **User Interface (for non-CLI):** If you're building an app or website, make it user-friendly and intuitive.
-7.  **Multiple Food Items (Advanced Bonus):** Can your solution handle an image with multiple food items on a plate?
-
-## Helpful Resources & APIs
-
-To get you started, here are some resources that might be useful. You are **not limited** to these and are encouraged to explore!
-
-### Food Image Datasets
-*(For training/inspiration if you go the custom ML route)*
-
-*   **Food-101:** [https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) (101 food categories, 101,000 images)
-*   **UECFood-100 / UECFood-256:** [http://foodcam.mobi/dataset.html](http://foodcam.mobi/dataset.html) (Japanese food primarily, good for object detection)
-*   **Recipe1M+:** [http://pic2recipe.csail.mit.edu/](http://pic2recipe.csail.mit.edu/) (Images and recipes)
-*   **Google Images / Flickr:** Can be used for scraping specific food images (be mindful of terms of service).
-
-### Nutrition Information Databases & APIs
-*(For calorie/macro lookup)*
-
-*   **USDA FoodData Central API:** [https://fdc.nal.usda.gov/api-guide.html](https://fdc.nal.usda.gov/api-guide.html) (Comprehensive US food composition database)
-*   **Edamam Food Database API:** [https://developer.edamam.com/food-database-api](https://developer.edamam.com/food-database-api) (Offers free tier for recipe analysis and food database lookup)
-*   **Spoonacular API:** [https://spoonacular.com/food-api](https://spoonacular.com/food-api) (Nutrition, recipes, food products, free tier available)
-*   **MyFitnessPal / CalorieKing / FatSecret:** While direct API access might be limited or paid, these websites are excellent sources for manual data collection or understanding how nutritional information is presented. Web scraping *could* be an option, but always respect `robots.txt` and terms of service.
-*   **Open Food Facts:** [https://world.openfoodfacts.org/](https://world.openfoodfacts.org/) (A collaborative, free, and open database of food products from around the world. They have an API.)
-
-### Machine Learning / AI Tools
-
-*   **TensorFlow / Keras:** For building and training custom models.
-*   **PyTorch:** Another popular deep learning framework.
-*   **OpenCV:** For image processing tasks.
-*   **Pre-trained Image Recognition Models:** (e.g., MobileNet, ResNet, InceptionV3 available via TensorFlow Hub, PyTorch Hub, etc.) These can often be fine-tuned for food recognition.
-*   **Cloud AI Services:** Google Cloud Vision AI, AWS Rekognition, Azure Computer Vision (these often have free tiers for experimentation and can perform object/food recognition out-of-the-box).
-
-## Getting Started: Git & Submission Workflow
-
-We will be using GitHub for version control and submission. Please follow these steps carefully.
-
-**This Repository (Main Project):** `https://github.com/WeCode-Community-Dev/foodsnap-ai`
-
-### 1. Fork the Repository
-*   Go to the main project repository: `https://github.com/WeCode-Community-Dev/foodsnap-ai`
-*   In the top-right corner of the page, click the "**Fork**" button.
-*   This will create a copy of the repository under your own GitHub account (e.g., `https://github.com/YOUR_USERNAME/foodsnap-ai`). This is *your* personal remote copy.
-
-### 2. Clone Your Forked Repository
-*   On your GitHub page for *your forked repository* (`https://github.com/YOUR_USERNAME/foodsnap-ai`), click the green "**Code**" button.
-*   Copy the HTTPS or SSH URL.
-*   Open your terminal or Git client and run:
-    ```bash
-    git clone https://github.com/YOUR_USERNAME/foodsnap-ai.git
-    cd foodsnap-ai
-    ```
-    (Replace `YOUR_USERNAME` with your actual GitHub username.)
-
-### 3. Create a New Branch
-*   It's crucial to work on a new branch rather than directly on `main` or `master`.
-*   Choose a descriptive branch name, for example, `feature/your-team-name` or `solution-john-doe`.
-*   In your terminal, inside the `foodsnap-ai` directory, run:
-    ```bash
-    git checkout -b feature/your-team-name
-    ```
-    (e.g., `git checkout -b feature/awesome-coders` or `git checkout -b solution-jane-doe`)
-*   You are now on your new branch. Verify by running `git branch`.
-
-### 4. Develop Your Project
-*   Start coding! Add your files, write your logic, and build your FoodSnap AI solution.
-*   Commit your changes frequently with clear commit messages:
-    ```bash
-    # Stage all new and modified files
-    git add .
-    # Or stage specific files
-    # git add path/to/your/file.py path/to/another/file.js
-
-    # Commit your changes
-    git commit -m "feat: Implement image upload functionality"
-    # Example commit types: feat, fix, docs, style, refactor, test, chore
-    ```
-
-### 5. Push Your Branch to Your Fork
-*   When you're ready to save your progress to *your remote fork on GitHub*, push your branch:
-    ```bash
-    git push origin feature/your-team-name
-    ```
-    (Replace `feature/your-team-name` with your actual branch name.)
-*   If it's the first time pushing this branch, Git might suggest a command like `git push --set-upstream origin feature/your-team-name`. Use that command.
-
-### 6. Submit Your Project (Create a Pull Request)
-*   Once your project is complete (or at a submittable stage), go to *your* forked repository on GitHub (`https://github.com/YOUR_USERNAME/foodsnap-ai`).
-*   You should see a prompt saying "`feature/your-team-name` had recent pushes". Click the "**Compare & pull request**" button.
-*   If you don't see the prompt, go to the "**Pull requests**" tab and click "**New pull request**".
-*   **Crucially, ensure the settings are:**
-    *   **Base repository:** `WeCode-Community-Dev/foodsnap-ai`
-    *   **Base branch:** `main` (or `master`, whichever is the default for this repository)
-    *   **Head repository:** `YOUR_USERNAME/foodsnap-ai`
-    *   **Compare branch:** `feature/your-team-name` (your development branch)
-*   Write a clear title and a detailed description for your Pull Request (PR). Include:
-    *   A brief overview of your solution.
-    *   Technologies used.
-    *   How to run/test your project (setup, commands, etc.).
-    *   Any known issues or limitations.
-    *   A link to a live demo if applicable (e.g., Heroku, Netlify, GitHub Pages).
-    *   Screenshots or a short video showcasing your project can be very helpful!
-*   Click "**Create pull request**".
-
-## What to Include in Your Submission
-*(In your project's directory, pushed to your branch and included in the PR)*
-
-*   **Source Code:** All the code for your project.
-*   **`README.md` (Your Project's README):** This is very important! Your project's `README.md` (different from this main hackathon `README.md`) should include:
-    *   Project Title & Team Name/Members (if applicable).
-    *   A brief description of your FoodSnap AI solution.
-    *   What features you implemented.
-    *   Tech stack used.
-    *   **Clear instructions on how to set up and run *your specific project* locally.** This includes dependencies, environment variables, build steps, and run commands.
-    *   Any API keys or environment variables needed (explain how to get them, but **DO NOT COMMIT ACTUAL KEYS** to the repository). Use a `.env.example` file to show what's needed.
-    *   Link to a live demo (if any).
-*   **(Optional but Recommended)** A short demo video or presentation slides (you can link these in your PR description or in your project's README).
-
-## Judging Criteria (General Pointers)
-
-While specific criteria might be announced, generally projects are evaluated on:
-
-*   **Functionality:** Does it work as intended? Does it achieve the core goal of calorie estimation from an image?
-*   **Accuracy:** How close are the calorie/macro estimations? (We understand this is complex!)
-*   **Innovation & Creativity:** Did you come up with a unique approach or an interesting feature?
-*   **Technical Implementation:** Quality of code, choice of technology, and complexity handled.
-*   **User Experience (UX/UI):** If applicable, is the application easy and pleasant to use?
-*   **Presentation/Demo:** How well you explain and showcase your project.
-*   **Adherence to Submission Guidelines:** Including a good project `README.md` and following the Git workflow.
-
-
----
-
-Good luck, innovators! We can't wait to see your FoodSnap AI creations. Remember to have fun, learn, and collaborate!
-
-**Happy Hacking!**
-The WeCode Community Dev Team
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diff --git a/data-processing.py b/data-processing.py
new file mode 100644
index 00000000..59e6f04b
--- /dev/null
+++ b/data-processing.py
@@ -0,0 +1,50 @@
+import pandas as pd
+from database import engine
+import csv
+from io import StringIO
+
+
+
+def psql_insert_copy(table, conn, keys, data_iter):
+    """
+    Execute SQL statement inserting data
+
+    Parameters
+    ----------
+    table : name of SQL table.
+    conn : sqlalchemy connection engine.
+    keys : list of str
+        Column names
+    data_iter : Iterable that iterates the values to be inserted
+    """
+    # gets a DBAPI connection that can provide a cursor
+    dbapi_conn = conn.connection
+    with dbapi_conn.cursor() as cur:
+        s_buf = StringIO()
+        writer = csv.writer(s_buf)
+        writer.writerows(data_iter)
+        s_buf.seek(0)
+
+        columns = ', '.join(['"{}"'.format(k) for k in keys])
+        if table.schema:
+            table_name = '{}.{}'.format(table.schema, table.name)
+        else:
+            table_name = table.name
+
+        sql = 'COPY {} ({}) FROM STDIN WITH CSV'.format(
+            table_name, columns)
+        cur.copy_expert(sql=sql, file=s_buf)
+
+df = pd.read_csv(r'C:\Users\GAFAR\Desktop\ai-project\web_scrap\cal_data.csv')
+
+df.to_sql(
+    name= 'nutrition_data_project',
+    con= engine,
+    if_exists= 'append',
+    index= False,
+    method= psql_insert_copy
+)
+
+print("DATA SAVED INTO TABLE SUCCESSFULLY !!!")
+
+# FASTEST METHOD TO STORE LARGE DATASET INTO DB.
\ No newline at end of file
diff --git a/database.py b/database.py
new file mode 100644
index 00000000..06e93aa6
--- /dev/null
+++ b/database.py
@@ -0,0 +1,12 @@
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+from sqlalchemy.ext.declarative import declarative_base
+
+
+URL_DATABASE = 'postgresql://postgres:123456@localhost:5432/ai_project_database'
+engine = create_engine(URL_DATABASE)
+
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+Base = declarative_base()
+
+
diff --git a/ml_model/__pycache__/model.cpython-311.pyc b/ml_model/__pycache__/model.cpython-311.pyc
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diff --git a/ml_model/model.py b/ml_model/model.py
index e37889eb..6c436cba 100644
--- a/ml_model/model.py
+++ b/ml_model/model.py
@@ -3,40 +3,39 @@
 import numpy as np
 
 
-def food_ai_model(image):
-    model = tf.keras.models.load_model('/home/acer/Downloads/indian_food_model.keras')
-    # model.summary()
-
-    # image=cv.imread(image, cv.IMREAD_COLOR_RGB)
-    img = cv.imdecode(np.frombuffer(image, dtype= np.uint8), cv.IMREAD_COLOR_RGB)    
-    image_resized = cv.resize(img, (180,180))
-    image = np.expand_dims(image_resized,axis=0)
-    # print(image.shape)
-
-    pred = model.predict(image)
-    # print(pred)
-
-    class_names = ['adhirasam', 'aloo_gobi', 'aloo_matar', 'aloo_methi', 'aloo_shimla_mirch', 'aloo_tikki', 'anarsa', 'ariselu', 
-                   'bandar_laddu', 'basundi', 'bhatura', 'bhindi_masala', 'biryani', 'boondi', 'butter_chicken', 'chak_hao_kheer', 
-                   'cham_cham', 'chana_masala', 'chapati', 'chhena_kheeri', 'chicken_razala', 'chicken_tikka', 'chicken_tikka_masala', 
-                   'chikki', 'daal_baati_churma', 'daal_puri', 'dal_makhani', 'dal_tadka', 'dharwad_pedha', 'doodhpak', 'dosa', 
-                   'double_ka_meetha', 'dum_aloo', 'gajar_ka_halwa', 'gavvalu', 'ghee_rice', 'ghevar', 'grilled_chicken', 'gulab_jamun', 
-                   'idli', 'imarti', 'jalebi', 'kachori', 'kadai_paneer', 'kadhi_pakoda', 'kajjikaya', 'kakinada_khaja', 'kalakand', 
-                   'karela_bharta', 'kofta', 'kuzhi_paniyaram', 'lassi', 'ledikeni', 'litti_chokha', 'lyangcha', 'maach_jhol', 
-                   'makki_di_roti_sarson_da_saag', 'malapua', 'misi_roti', 'misti_doi', 'modak', 'mysore_pak', 'naan', 'navrattan_korma', 
-                   'palak_paneer', 'paneer_butter_masala', 'phirni', 'pithe', 'poha', 'poornalu', 'pootharekulu', 'puttu', 'qubani_ka_meetha', 
-                   'rabri', 'ras_malai', 'rasgulla', 'rice', 'sandesh', 'shankarpali', 'sheer_korma', 'sheera', 'shrikhand', 'sohan_halwa', 
-                   'sohan_papdi', 'sutar_feni', 'unni_appam']
-
-    output_class = class_names[np.argmax(pred)]
-    # print("The predicted class is", output_class)
-    return output_class
+def food_ai_model():
+    model = tf.keras.models.load_model(r'c:\Users\GAFAR\Downloads\food_model.keras')
+
+    def predict_food_name(image):
+        # image=cv.imread(image, cv.IMREAD_COLOR_RGB)
+        img = cv.imdecode(np.frombuffer(image, dtype= np.uint8), cv.IMREAD_COLOR_RGB)    
+        image_resized = cv.resize(img, (180,180))
+        image = np.expand_dims(image_resized,axis=0)
+
+        pred = model.predict(image)
+
+        class_names = ['adhirasam', 'aloo matar', 'aloo shimla mirch', 'aloo tikki', 'anarsa', 'apple fruit', 'ariselu',
+                        'banana fruit', 'bandar laddu', 'basundi', 'bhatura', 'bhindi masala', 'biryani', 'boiled egg',
+                        'boiled rice', 'boondi raita', 'butter chicken', 'carrot', 'cauliflower', 'cham cham',
+                        'chana masala', 'chapati', 'cheese pizza', 'chicken stew', 'chicken tikka', 'chicken tikka masala',
+                        'chikki', 'daal baati churma', 'daal puri', 'dal makhani', 'dal tadka', 'dharwad pedha', 'doodhpak',
+                        'double ka meetha', 'dum aloo', 'fish curry', 'gajar ka halwa', 'gavvalu', 'ghee rice', 'ghevar',
+                        'gobi 65', 'gulab jamun with khoya', 'hot tea', 'idli', 'imarti', 'jalebi', 'kachori', 
+                        'kadhi pakoda', 'kajjikaya', 'kakinada khaja', 'kalakand', 'karela bharta', 'kofta', 
+                        'kuzhi paniyaram', 'lassi', 'litti chokha', 'makki ki roti', 'malapua', 'misti doi', 'modak', 
+                        'mysore pak', 'naan', 'navrattan korma', 'orange fruit', 'palak paneer', 'paneer butter masala', 
+                        'pea paneer curry', 'phirni', 'pithe', 'plain dosa', 'poha', 'poornalu', 'pootharekulu', 'rabri', 
+                        'ras malai', 'rasgulla', 'rice puttu', 'sandesh', 'shankarpali', 'sheer korma', 'sheera', 'shrikhand', 
+                        'sohan halwa', 'sohan papdi', 'tandoori chicken', 'tomatoes', 'unni appam']
+
+
+        output_class = class_names[np.argmax(pred)]
+        return output_class
+    
+    return predict_food_name
      
 
 
-# image = '/home/acer/Downloads/archive/Indian Food Images/idli/fbbfimages.jpeg'
-
-# food_ai_model(image)
 
 
 
diff --git a/models.py b/models.py
new file mode 100644
index 00000000..fac58cf4
--- /dev/null
+++ b/models.py
@@ -0,0 +1,21 @@
+from sqlalchemy import Column, Integer, String
+from database import Base
+
+class CalorieNutritionData(Base):
+    __tablename__ = 'nutrition_data_project'
+
+    id = Column(Integer, primary_key=True, index=True)
+    dish_name = Column(String, index= True)
+    calories = Column(String)
+    carbohydrates = Column(String, default=None)
+    protein = Column(String, default=None)
+    fats = Column(String, default=None)
+    free_sugar = Column(String, default=None)
+    fibre = Column(String, default=None)
+    sodium = Column(String, default=None)
+    calcium = Column(String, default=None)
+    iron = Column(String, default=None)
+    vitamin_c  = Column(String, default=None)
+    folate = Column(String, default=None)
+
+
diff --git a/requirements.txt b/requirements.txt
index c71383d0..60003ffb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,7 @@
 fastapi==0.115.13
 uvicorn==0.34.3
 tensorflow==2.19.0
-opencv-python==4.11.0.86
\ No newline at end of file
+opencv-python==4.11.0.86
+beautifulsoup4==4.13.4
+SQLAlchemy==2.0.41
+psycopg2==2.9.10
\ No newline at end of file
diff --git a/script.py b/script.py
index 29bc1e31..b6cf3613 100644
--- a/script.py
+++ b/script.py
@@ -1,22 +1,80 @@
-from fastapi import FastAPI, UploadFile
+from fastapi import FastAPI, UploadFile, HTTPException, Depends
 from starlette import status
 from ml_model.model import food_ai_model
+from typing import Annotated
+from contextlib import asynccontextmanager
 
-app = FastAPI()
+import models
+from database import engine, SessionLocal
+from sqlalchemy.orm import Session
 
+
+ml_models = {}
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):    
+    print('Loading BASE model')
+    ml_models["food_classifier"] = food_ai_model() # code to execute when app is loading
+    yield    
+    print('Shutting down the model...')  # code to execute when app is shutting down
+
+app = FastAPI(lifespan=lifespan)
+models.Base.metadata.create_all(bind=engine)   # CREATE TABLES
+
+# DB CALL
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
+
+
+
+# ENDPOINTS
 @app.get('/')
 async def project():
 
     return ({"message": "Food snap Project"})
 
 
+async def calculate_nutritional_values(quantity, macronutrients):
+    value = ((macronutrients * 100) * quantity)/100
+    return value
+
+
 @app.post('/foodsnap/v1/upload', status_code= status.HTTP_200_OK)
-async def fetch_data(image: UploadFile, quantity: int):
+async def fetch_data(image: UploadFile, quantity: int, db: db_dependency):  
 
     uploaded_image = await image.read()
-    food_name = food_ai_model(uploaded_image)
+    food_name = ml_models["food_classifier"](uploaded_image)
+    print(food_name)
     food_quantity = quantity
 
-    return ({"name": food_name, "quantity": food_quantity})
+    food_data = db.query(models.CalorieNutritionData).filter(models.CalorieNutritionData.dish_name == food_name).first()
+    
+    if food_data:
+        calories = float(food_data.calories)
+        carbohydrates = float(food_data.carbohydrates)
+        protein = float(food_data.protein)
+        fats = float(food_data.fats)
+
+        result = {
+            "Dish Name": food_name,
+            "Quantity": food_quantity,
+            "Calories": f'{await calculate_nutritional_values(food_quantity, calories)}(g)',
+            "Carbohydrates" : f'{await calculate_nutritional_values(food_quantity, carbohydrates)}(g)',
+            "Protein" : f'{await calculate_nutritional_values(food_quantity, protein)}(g)',
+            "Fats" : f'{await calculate_nutritional_values(food_quantity, fats)}(g)' 
+        }
+
+        return result
+    
+    else:
+        return HTTPException(status_code=404, detail= "The specified dish could not be found.")
+
+
 
 

From 3c9f4ee972ac915d9bbde22676e80399556b3861 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Tue, 22 Jul 2025 14:37:09 +0530
Subject: [PATCH 03/13] refactor: Improved code and docs

---
 __pycache__/models.cpython-311.pyc          | Bin 1370 -> 1373 bytes
 __pycache__/script.cpython-311.pyc          | Bin 4064 -> 5171 bytes
 db/__pycache__/database.cpython-311.pyc     | Bin 0 -> 651 bytes
 db/__pycache__/models.cpython-311.pyc       | Bin 0 -> 1376 bytes
 data-processing.py => db/data-processing.py |   2 +-
 database.py => db/database.py               |   0
 models.py => db/models.py                   |   2 +-
 ml_model/__pycache__/model.cpython-311.pyc  | Bin 2437 -> 2988 bytes
 ml_model/model.py                           |  24 +-
 ml_model/resnet50_finetune.ipynb            | 344 ++++++++++++++++++++
 script.py                                   |  43 ++-
 11 files changed, 389 insertions(+), 26 deletions(-)
 create mode 100644 db/__pycache__/database.cpython-311.pyc
 create mode 100644 db/__pycache__/models.cpython-311.pyc
 rename data-processing.py => db/data-processing.py (97%)
 rename database.py => db/database.py (100%)
 rename models.py => db/models.py (95%)
 create mode 100644 ml_model/resnet50_finetune.ipynb

diff --git a/__pycache__/models.cpython-311.pyc b/__pycache__/models.cpython-311.pyc
index ecdd1e0c78040713b3081719dff6bb86c02a2fe3..4b2afa4f9f8e1eb7d70366d17dad7e67e04960f9 100644
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diff --git a/__pycache__/script.cpython-311.pyc b/__pycache__/script.cpython-311.pyc
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HcmV?d00001

diff --git a/data-processing.py b/db/data-processing.py
similarity index 97%
rename from data-processing.py
rename to db/data-processing.py
index 59e6f04b..abe25809 100644
--- a/data-processing.py
+++ b/db/data-processing.py
@@ -1,5 +1,5 @@
 import pandas as pd
-from database import engine
+from db.database import engine
 import csv
 from io import StringIO
 
diff --git a/database.py b/db/database.py
similarity index 100%
rename from database.py
rename to db/database.py
diff --git a/models.py b/db/models.py
similarity index 95%
rename from models.py
rename to db/models.py
index fac58cf4..68087c12 100644
--- a/models.py
+++ b/db/models.py
@@ -1,5 +1,5 @@
 from sqlalchemy import Column, Integer, String
-from database import Base
+from db.database import Base
 
 class CalorieNutritionData(Base):
     __tablename__ = 'nutrition_data_project'
diff --git a/ml_model/__pycache__/model.cpython-311.pyc b/ml_model/__pycache__/model.cpython-311.pyc
index a4f6ab27d8dafa2ca7a07facb29deb34b680bf3a..0dda09db622b840f18aef980e6cfb38488532ca3 100644
GIT binary patch
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diff --git a/ml_model/model.py b/ml_model/model.py
index 6c436cba..6c875a55 100644
--- a/ml_model/model.py
+++ b/ml_model/model.py
@@ -4,6 +4,19 @@
 
 
 def food_ai_model():
+    """
+    Loads a pre-trained food classification model and returns a prediction function.
+
+    This function initializes a TensorFlow Keras model from a specified path.
+    It then defines and returns an inner function, `predict_food_name`, which
+    takes an image (as bytes), preprocesses it, and uses the loaded model to
+    predict the food item's name from a predefined list of classes.
+
+    Returns:
+        function: A function `predict_food_name` that takes an image (bytes)
+                and returns the predicted food name (string).
+    """
+
     model = tf.keras.models.load_model(r'c:\Users\GAFAR\Downloads\food_model.keras')
 
     def predict_food_name(image):
@@ -33,13 +46,4 @@ def predict_food_name(image):
         return output_class
     
     return predict_food_name
-     
-
-
-
-
-
-
-
-
-
+     
\ No newline at end of file
diff --git a/ml_model/resnet50_finetune.ipynb b/ml_model/resnet50_finetune.ipynb
new file mode 100644
index 00000000..5ea61c07
--- /dev/null
+++ b/ml_model/resnet50_finetune.ipynb
@@ -0,0 +1,344 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jK4dl_zNjmXu"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import PIL\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.python.keras.layers import Dense, Input, Flatten\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from tensorflow.keras.models import Model\n",
+        "# from tensorflow.keras.applications import ResNet50\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nTdVTk0kj_Y-"
+      },
+      "source": [
+        "# Setuping Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Twh9rwHuj-NQ",
+        "collapsed": true
+      },
+      "outputs": [],
+      "source": [
+        "import zipfile\n",
+        "\n",
+        "extracted_file = \"/content/indian_food_images\"\n",
+        "\n",
+        "with zipfile.ZipFile(\"/content/drive/MyDrive/ai-project-dataset/Indian Food Images.zip\", 'r') as zip_ref:\n",
+        "  zip_ref.extractall(extracted_file)\n",
+        "\n",
+        "# /content/drive/MyDrive/ai-project-dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3GC362e7dR9M",
+        "outputId": "27a63b0f-f51f-44ad-f8c6-3ae9ea7f9a6b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PosixPath('/content/indian_food_images/Indian Food Images')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "import pathlib\n",
+        "\n",
+        "data_dir = pathlib.Path(\"/content/indian_food_images/Indian Food Images\")\n",
+        "data_dir"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331
+        },
+        "id": "4ICRx796btqy",
+        "outputId": "73e65284-4d06-478c-e6e4-0aa68e205972",
+        "collapsed": true
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "50\n",
+            "[PosixPath('/content/indian_food_images/Indian Food Images/bhatura/9bdae15cf5.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/6b7d6a02b0.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/8b37f8729e.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/10e2670c3b.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/3f2be0dce2.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/9ff0e71a7b.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/5e5fcfafab.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/8a31202469.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/6ca43de67f.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/7f4e67b3a2.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/2b9a862167.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/7dfec3ba4c.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/1a1920e559.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/1ed8f97fe2.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/5d49d7fc8c.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/0c336b5378.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/4bf6c18d87.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/0b3ed8d096.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/6b717b959f.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/016dd09bb1.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/23c3ce067e.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/7d511d084f.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/1bb2a0ca01.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/6bfc35793f.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/25f97b0569.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/0e84306e25.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/2ec3c7d2d5.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/9f68d9dfe2.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/3db3e71567.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/9bcde83d8d.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/4e052c4c31.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/8aacc3a978.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/5fa68259c7.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/24e4bcbbd9.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/17afbffe7d.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/8a31746a32.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/5cbcf3fdae.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/5f5c3e7090.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/4a2173df91.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/18e4d65d85.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/005c62055f.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/8d41747def.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/0da0ea24b2.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/8e4b799a9a.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/5ecb6ad613.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/1ce23e9981.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/4fe1f53b08.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/14fcac8706.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/14b6125138.jpg'), PosixPath('/content/indian_food_images/Indian Food Images/bhatura/26af243f03.jpg')]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=194x259>"
+            ],
+            "image/png": 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\n",
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\n"
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "food_images = list(data_dir.glob('bhatura/*'))\n",
+        "print(len(food_images))\n",
+        "print(food_images)\n",
+        "PIL.Image.open(str(food_images[0]))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_yzHPVqYcqJL",
+        "outputId": "4d44531a-cf64-494b-8141-a4aac98334ed"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 4427 files belonging to 87 classes.\n",
+            "Using 3542 files for training.\n"
+          ]
+        }
+      ],
+      "source": [
+        "img_height,img_width=180,180\n",
+        "batch_size=32\n",
+        "\n",
+        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
+        "  data_dir,\n",
+        "  validation_split=0.2,\n",
+        "  subset=\"training\",\n",
+        "  seed=123,   #randomness\n",
+        "  image_size=(img_height, img_width),\n",
+        "  batch_size=batch_size)  #chunk of data passed into model at a time"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
+        "  data_dir,\n",
+        "  validation_split=0.2,\n",
+        "  subset=\"validation\",\n",
+        "  seed=123,\n",
+        "  image_size=(img_height, img_width),\n",
+        "  batch_size=batch_size)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cckvvzwlwZsu",
+        "outputId": "e4ae8e95-7137-472b-fa2f-24e837410ac5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found 4427 files belonging to 87 classes.\n",
+            "Using 885 files for validation.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class_names = len(train_ds.class_names)\n",
+        "cls_nm = train_ds.class_names\n",
+        "print(cls_nm)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GbhCLL6_wfM4",
+        "outputId": "1928ad23-91f6-4f1d-a09e-361e083a448e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['adhirasam', 'aloo matar', 'aloo shimla mirch', 'aloo tikki', 'anarsa', 'apple fruit', 'ariselu', 'banana fruit', 'bandar laddu', 'basundi', 'bhatura', 'bhindi masala', 'biryani', 'boiled egg', 'boiled rice', 'boondi raita', 'butter chicken', 'carrot', 'cauliflower', 'cham cham', 'chana masala', 'chapati', 'cheese pizza', 'chicken stew', 'chicken tikka', 'chicken tikka masala', 'chikki', 'daal baati churma', 'daal puri', 'dal makhani', 'dal tadka', 'dharwad pedha', 'doodhpak', 'double ka meetha', 'dum aloo', 'fish curry', 'gajar ka halwa', 'gavvalu', 'ghee rice', 'ghevar', 'gobi 65', 'gulab jamun with khoya', 'hot tea', 'idli', 'imarti', 'jalebi', 'kachori', 'kadhi pakoda', 'kajjikaya', 'kakinada khaja', 'kalakand', 'karela bharta', 'kofta', 'kuzhi paniyaram', 'lassi', 'litti chokha', 'makki ki roti', 'malapua', 'misti doi', 'modak', 'mysore pak', 'naan', 'navrattan korma', 'orange fruit', 'palak paneer', 'paneer butter masala', 'pea paneer curry', 'phirni', 'pithe', 'plain dosa', 'poha', 'poornalu', 'pootharekulu', 'rabri', 'ras malai', 'rasgulla', 'rice puttu', 'sandesh', 'shankarpali', 'sheer korma', 'sheera', 'shrikhand', 'sohan halwa', 'sohan papdi', 'tandoori chicken', 'tomatoes', 'unni appam']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Training Model"
+      ],
+      "metadata": {
+        "id": "1l0hF31Dxk47"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "input_tensor = tf.keras.Input(shape=(180, 180, 3))\n",
+        "\n",
+        "base_model = tf.keras.applications.ResNet50(\n",
+        "    include_top=False,\n",
+        "    weights='imagenet',\n",
+        "    input_tensor=input_tensor,  # Must be tf.keras.Input\n",
+        "    pooling='avg'\n",
+        ")\n",
+        "\n",
+        "base_model.trainable = False\n",
+        "\n",
+        "x = base_model.output\n",
+        "x = tf.keras.layers.Flatten()(x)\n",
+        "x = tf.keras.layers.Dense(512, activation='relu')(x)\n",
+        "output_tensor = tf.keras.layers.Dense(87, activation='softmax')(x)\n",
+        "\n",
+        "model = tf.keras.Model(inputs=input_tensor, outputs=output_tensor)\n",
+        "\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "id": "L639e5wUv53r",
+        "collapsed": true
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(\n",
+        "    optimizer=Adam(learning_rate=0.001),   #small value = slower, stable learning\n",
+        "    loss='sparse_categorical_crossentropy',\n",
+        "    metrics=['accuracy']\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "DKgJ8aM4Xn-J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#one full pass through the entire training dataset(1 epoch)\n",
+        "#validation_data =  Dataset used to evaluate performance after each epoch\n",
+        "\n",
+        "epochs=10\n",
+        "history = model.fit(\n",
+        "  train_ds,\n",
+        "  validation_data=val_ds,\n",
+        "  epochs=epochs\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "ZNCN41Nb0Jgl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3309ed34-486d-4c6f-8007-14e78a053827"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m573s\u001b[0m 5s/step - accuracy: 0.1479 - loss: 4.0349 - val_accuracy: 0.3525 - val_loss: 2.5790\n",
+            "Epoch 2/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m556s\u001b[0m 5s/step - accuracy: 0.5381 - loss: 1.7626 - val_accuracy: 0.4316 - val_loss: 2.2191\n",
+            "Epoch 3/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m527s\u001b[0m 5s/step - accuracy: 0.7218 - loss: 1.0611 - val_accuracy: 0.4610 - val_loss: 2.1351\n",
+            "Epoch 4/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m603s\u001b[0m 5s/step - accuracy: 0.8296 - loss: 0.6809 - val_accuracy: 0.4768 - val_loss: 2.0970\n",
+            "Epoch 5/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m571s\u001b[0m 5s/step - accuracy: 0.9008 - loss: 0.4201 - val_accuracy: 0.5017 - val_loss: 2.1226\n",
+            "Epoch 6/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m604s\u001b[0m 5s/step - accuracy: 0.9374 - loss: 0.2896 - val_accuracy: 0.4994 - val_loss: 2.1443\n",
+            "Epoch 7/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m559s\u001b[0m 5s/step - accuracy: 0.9706 - loss: 0.1753 - val_accuracy: 0.5232 - val_loss: 2.0536\n",
+            "Epoch 8/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m567s\u001b[0m 5s/step - accuracy: 0.9834 - loss: 0.1197 - val_accuracy: 0.5209 - val_loss: 2.1182\n",
+            "Epoch 9/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m562s\u001b[0m 5s/step - accuracy: 0.9919 - loss: 0.0901 - val_accuracy: 0.5401 - val_loss: 2.1051\n",
+            "Epoch 10/10\n",
+            "\u001b[1m111/111\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m566s\u001b[0m 5s/step - accuracy: 0.9936 - loss: 0.0631 - val_accuracy: 0.5209 - val_loss: 2.1849\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save('food_model.keras')"
+      ],
+      "metadata": {
+        "id": "ST1LVkfZTV-L"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/script.py b/script.py
index b6cf3613..bf5d8076 100644
--- a/script.py
+++ b/script.py
@@ -1,19 +1,22 @@
-from fastapi import FastAPI, UploadFile, HTTPException, Depends
+from fastapi import FastAPI, UploadFile, HTTPException, Depends, Query
 from starlette import status
 from ml_model.model import food_ai_model
 from typing import Annotated
 from contextlib import asynccontextmanager
 
-import models
-from database import engine, SessionLocal
+from db import models
+from db.database import engine, SessionLocal
 from sqlalchemy.orm import Session
 
 
 ml_models = {}
+MODEL = 'RestNet50'
 
 @asynccontextmanager
-async def lifespan(app: FastAPI):    
-    print('Loading BASE model')
+async def lifespan(app: FastAPI):   
+    """ Calling Model only Single time"""
+
+    print(f'Loading {MODEL} model')
     ml_models["food_classifier"] = food_ai_model() # code to execute when app is loading
     yield    
     print('Shutting down the model...')  # code to execute when app is shutting down
@@ -28,7 +31,6 @@ def get_db():
         yield db
     finally:
         db.close()
-
 db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
 
 
@@ -36,21 +38,36 @@ def get_db():
 # ENDPOINTS
 @app.get('/')
 async def project():
-
     return ({"message": "Food snap Project"})
 
 
 async def calculate_nutritional_values(quantity, macronutrients):
+    """Calculates the nutritional value based on quantity and macronutrients"""
+
     value = ((macronutrients * 100) * quantity)/100
     return value
 
 
 @app.post('/foodsnap/v1/upload', status_code= status.HTTP_200_OK)
-async def fetch_data(image: UploadFile, quantity: int, db: db_dependency):  
+async def fetch_data(db: db_dependency,image: UploadFile, quantity: int = Query(gt=0)):  
+    """ 
+    Uploads a food image, classifies it using an ML model, retrieves its nutritional information from the database,
+    and calculates the total nutritional values based on the provided quantity.
+    
+    Returns:
+    A dictionary with the food's name, quantity, and calculated calories, carbs, protein, and fats.
+    """
 
     uploaded_image = await image.read()
-    food_name = ml_models["food_classifier"](uploaded_image)
-    print(food_name)
+
+    try:
+        food_name = ml_models["food_classifier"](uploaded_image)
+        if not food_name:
+            raise ValueError('Food classification failed or returned empty result.')
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return HTTPException(status_code=500, detail='Could not classify the food item. Please try again or with a clearer image')
+    
     food_quantity = quantity
 
     food_data = db.query(models.CalorieNutritionData).filter(models.CalorieNutritionData.dish_name == food_name).first()
@@ -69,11 +86,9 @@ async def fetch_data(image: UploadFile, quantity: int, db: db_dependency):
             "Protein" : f'{await calculate_nutritional_values(food_quantity, protein)}(g)',
             "Fats" : f'{await calculate_nutritional_values(food_quantity, fats)}(g)' 
         }
-
-        return result
-    
+        return result    
     else:
-        return HTTPException(status_code=404, detail= "The specified dish could not be found.")
+        return HTTPException(status_code=404, detail= f"Nutritional data for '{food_name}' could not be found.")
 
 
 

From c818fdc119e8c0ed00b7a09bebb6b409b904e13c Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Tue, 22 Jul 2025 14:58:11 +0530
Subject: [PATCH 04/13] docs: readme file updated

---
 README.md | 52 +++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 51 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d591f8f3..10d0980f 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,53 @@
-# FoodSnap AI - Project
+# 🥗 FoodSnap AI
+
+**FoodSnap AI** is a lightweight FastAPI application that enables users to obtain nutritional information about food items by simply uploading an image. It uses a pre-trained machine learning model to classify food images and retrieves detailed macronutrient data from a connected database.
+
+---
+
+##  Features
+
+-  **Intelligent Food Classification**  
+  Upload an image, and our integrated ResNet50 model identifies the food item.
+
+-  **Comprehensive Nutritional Data**  
+  Get calories, carbohydrates, protein, and fats for the identified food.
+
+-  **Quantity-Based Calculations**  
+  Receive accurate nutritional values scaled to your specified quantity.
+
+-  **Efficient Model Management**  
+  The AI model is loaded once at startup for fast subsequent processing.
+
+-  **Robust API**  
+  Exposes a well-structured `/foodsnap/v1/upload` endpoint for seamless integration.
+
+---
+
+##  Getting Started
+
+Follow the steps below to set up and run **FoodSnap AI** on your local machine.
+
+---
+
+## Tech Stack
+
+- FastAPI
+- TensorFlow
+- OpenCV
+- SQLAlchemy + PostgreSQL
+
+##  Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/your-username/foodsnap-ai.git
+cd foodsnap-ai
+
+# Create and activate a virtual environment
+python -m venv venv
+source venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
 
 

From 097e6937d9f1fb2e04bfc917bc780a637ee76c83 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <122769943+gafar8281@users.noreply.github.com>
Date: Tue, 22 Jul 2025 15:00:19 +0530
Subject: [PATCH 05/13] Update README.md

---
 README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/README.md b/README.md
index 10d0980f..8b57e868 100644
--- a/README.md
+++ b/README.md
@@ -27,8 +27,6 @@
 
 Follow the steps below to set up and run **FoodSnap AI** on your local machine.
 
----
-
 ## Tech Stack
 
 - FastAPI

From 224443a4782a6efb69f66b05312120ab1a57185f Mon Sep 17 00:00:00 2001
From: Abdul Gafar <122769943+gafar8281@users.noreply.github.com>
Date: Tue, 22 Jul 2025 15:05:47 +0530
Subject: [PATCH 06/13] Update README.md

---
 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 8b57e868..c3106b4b 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,12 @@
 -  **Robust API**  
   Exposes a well-structured `/foodsnap/v1/upload` endpoint for seamless integration.
 
+-  **Data Population (Web Scraping)**
+  Nutritional data is fetched and updated in the database using web scraping techniques.
+
+  
+
+
 ---
 
 ##  Getting Started
@@ -33,6 +39,8 @@ Follow the steps below to set up and run **FoodSnap AI** on your local machine.
 - TensorFlow
 - OpenCV
 - SQLAlchemy + PostgreSQL
+- Beautiful Soup
+- Selenium
 
 ##  Installation
 

From 1bc4da254a25fd350208549ae3f3d67ba83f932a Mon Sep 17 00:00:00 2001
From: Abdul Gafar <122769943+gafar8281@users.noreply.github.com>
Date: Tue, 22 Jul 2025 15:09:05 +0530
Subject: [PATCH 07/13] Update README.md

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index c3106b4b..4108818f 100644
--- a/README.md
+++ b/README.md
@@ -37,10 +37,12 @@ Follow the steps below to set up and run **FoodSnap AI** on your local machine.
 
 - FastAPI
 - TensorFlow
-- OpenCV
-- SQLAlchemy + PostgreSQL
+- ResNet50 (pre-trained model)
 - Beautiful Soup
 - Selenium
+- Pandas
+- SQLAlchemy + PostgreSQL
+
 
 ##  Installation
 

From 4d217db122049ccc346f87a1065ae99fda640a14 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Thu, 24 Jul 2025 18:11:39 +0530
Subject: [PATCH 08/13] feat: Implemented logging functionality

---
 __pycache__/script.cpython-311.pyc | Bin 5171 -> 6706 bytes
 script.py                          |  28 +++++++++++++++++++++++++++-
 2 files changed, 27 insertions(+), 1 deletion(-)

diff --git a/__pycache__/script.cpython-311.pyc b/__pycache__/script.cpython-311.pyc
index 53c092bbd9fd86c2147de34bf2016bc67b23447c..eeb532e5d6cb16281ada1e30b86a865a53e998b9 100644
GIT binary patch
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diff --git a/script.py b/script.py
index bf5d8076..50036ea4 100644
--- a/script.py
+++ b/script.py
@@ -1,12 +1,16 @@
-from fastapi import FastAPI, UploadFile, HTTPException, Depends, Query
+from fastapi import FastAPI, UploadFile, HTTPException, Depends, Query, Request
 from starlette import status
 from ml_model.model import food_ai_model
 from typing import Annotated
 from contextlib import asynccontextmanager
+from starlette.middleware.base import BaseHTTPMiddleware
 
 from db import models
 from db.database import engine, SessionLocal
 from sqlalchemy.orm import Session
+import time
+import logging
+
 
 
 ml_models = {}
@@ -24,6 +28,7 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 models.Base.metadata.create_all(bind=engine)   # CREATE TABLES
 
+
 # DB CALL
 def get_db():
     db = SessionLocal()
@@ -34,6 +39,26 @@ def get_db():
 db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
 
 
+# LOGGING - SETUP
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class LoggingMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        start_time = time.time()
+        
+        # Log incoming request
+        logger.info(f"➡️ {request.method} {request.url.path}")        
+        response = await call_next(request)        
+        process_time = round((time.time() - start_time) * 1000, 2)
+        
+        # Log response status and duration
+        logger.info(f"⬅️ {request.method} {request.url.path} - {response.status_code} ({process_time} ms)")
+        
+        return response
+
+app.add_middleware(LoggingMiddleware)
+
 
 # ENDPOINTS
 @app.get('/')
@@ -93,3 +118,4 @@ async def fetch_data(db: db_dependency,image: UploadFile, quantity: int = Query(
 
 
 
+

From b01d7ab266fce00a891c1773f305d1c2efa4bf3c Mon Sep 17 00:00:00 2001
From: Abdul Gafar <122769943+gafar8281@users.noreply.github.com>
Date: Sat, 26 Jul 2025 23:44:31 +0530
Subject: [PATCH 09/13] feat : Implement logging feature

---
 script.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/script.py b/script.py
index bf5d8076..45e2f15b 100644
--- a/script.py
+++ b/script.py
@@ -3,6 +3,7 @@
 from ml_model.model import food_ai_model
 from typing import Annotated
 from contextlib import asynccontextmanager
+from starlette.middleware.base import BaseHTTPMiddleware
 
 from db import models
 from db.database import engine, SessionLocal
@@ -33,6 +34,24 @@ def get_db():
         db.close()
 db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
 
+# LOGGING - SETUP
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class LoggingMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        start_time = time.time()        
+        # Log incoming request
+        logger.info(f"➡️ {request.method} {request.url.path}")        
+        response = await call_next(request)        
+        process_time = round((time.time() - start_time) * 1000, 2)
+        
+        # Log response status and duration
+        logger.info(f"⬅️ {request.method} {request.url.path} - {response.status_code} ({process_time} ms)")
+        
+        return response
+
+app.add_middleware(LoggingMiddleware)
 
 
 # ENDPOINTS

From 64c373f19b3dbccff1fb2419f5f870822038541a Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Sun, 27 Jul 2025 00:00:08 +0530
Subject: [PATCH 10/13] fix: logging

---
 script.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/script.py b/script.py
index 50036ea4..98d0fb96 100644
--- a/script.py
+++ b/script.py
@@ -84,6 +84,7 @@ async def fetch_data(db: db_dependency,image: UploadFile, quantity: int = Query(
     """
 
     uploaded_image = await image.read()
+    
 
     try:
         food_name = ml_models["food_classifier"](uploaded_image)

From e0044e012b9bae57c5ab7d71b987c713715ff3bf Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Sat, 2 Aug 2025 22:35:07 +0530
Subject: [PATCH 11/13] fix: logging request

---
 script.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/script.py b/script.py
index daf250cf..e2efd7eb 100644
--- a/script.py
+++ b/script.py
@@ -7,6 +7,10 @@
 from db import models
 from db.database import engine, SessionLocal
 from sqlalchemy.orm import Session
+from starlette.middleware.base import BaseHTTPMiddleware
+import time
+import logging
+
 
 
 ml_models = {}
@@ -24,7 +28,6 @@ async def lifespan(app: FastAPI):
 app = FastAPI(lifespan=lifespan)
 models.Base.metadata.create_all(bind=engine)   # CREATE TABLES
 
-
 # DB CALL
 def get_db():
     db = SessionLocal()
@@ -34,6 +37,26 @@ def get_db():
         db.close()
 db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
 
+# Configure basic logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class LoggingMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        start_time = time.time()
+        
+        # Log incoming request
+        logger.info(f"➡️ {request.method} {request.url.path}")        
+        response = await call_next(request)        
+        process_time = round((time.time() - start_time) * 1000, 2)
+        
+        # Log response status and duration
+        logger.info(f"⬅️ {request.method} {request.url.path} - {response.status_code} ({process_time} ms)")        
+        return response
+
+app.add_middleware(LoggingMiddleware)
+
+
 
 # ENDPOINTS
 @app.get('/')

From 25f02f8ba653d1d0058b80e25ce18fcad8ee4670 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Sat, 2 Aug 2025 22:37:43 +0530
Subject: [PATCH 12/13] docs: logging

---
 script.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/script.py b/script.py
index e2efd7eb..08313a13 100644
--- a/script.py
+++ b/script.py
@@ -12,7 +12,6 @@
 import logging
 
 
-
 ml_models = {}
 MODEL = 'RestNet50'
 
@@ -37,7 +36,7 @@ def get_db():
         db.close()
 db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
 
-# Configure basic logging
+# Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 

From c2aba5f2103a9d2de7b10b073b05ec9ec92a96b1 Mon Sep 17 00:00:00 2001
From: Abdul Gafar <abdulgafar8281@gmail.com>
Date: Sat, 8 Nov 2025 12:50:09 +0530
Subject: [PATCH 13/13] first commit

---
 .gitignore                                    |   1 +
 README.md                                     |   3 +
 __pycache__/config.cpython-311.pyc            | Bin 0 -> 1096 bytes
 __pycache__/main.cpython-311.pyc              | Bin 0 -> 2018 bytes
 __pycache__/script.cpython-311.pyc            | Bin 6706 -> 6872 bytes
 config.py                                     |  22 ++++
 db/__pycache__/database.cpython-311.pyc       | Bin 651 -> 644 bytes
 db/__pycache__/models.cpython-311.pyc         | Bin 1376 -> 1376 bytes
 db/database.py                                |  10 +-
 main.py                                       |  51 ++++++++
 ml_model/__pycache__/model.cpython-311.pyc    | Bin 2988 -> 3039 bytes
 ml_model/model.py                             |   5 +-
 requirements.txt                              |   3 +-
 routes/__pycache__/foodsnap.cpython-311.pyc   | Bin 0 -> 4230 bytes
 routes/foodsnap.py                            |  83 ++++++++++++
 script.py                                     | 119 ------------------
 .../populate_database.py                      |  27 ++--
 .../nutrition_service.cpython-311.pyc         | Bin 0 -> 1589 bytes
 services/nutrition_service.py                 |  21 ++++
 19 files changed, 206 insertions(+), 139 deletions(-)
 create mode 100644 .gitignore
 create mode 100644 __pycache__/config.cpython-311.pyc
 create mode 100644 __pycache__/main.cpython-311.pyc
 create mode 100644 config.py
 create mode 100644 main.py
 create mode 100644 routes/__pycache__/foodsnap.cpython-311.pyc
 create mode 100644 routes/foodsnap.py
 delete mode 100644 script.py
 rename db/data-processing.py => scripts/populate_database.py (67%)
 create mode 100644 services/__pycache__/nutrition_service.cpython-311.pyc
 create mode 100644 services/nutrition_service.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..2eea525d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.env
\ No newline at end of file
diff --git a/README.md b/README.md
index 4108818f..4520cccf 100644
--- a/README.md
+++ b/README.md
@@ -58,4 +58,7 @@ source venv\Scripts\activate
 # Install dependencies
 pip install -r requirements.txt
 
+# Setup .env file
 
+# Run
+python main.py
diff --git a/__pycache__/config.cpython-311.pyc b/__pycache__/config.cpython-311.pyc
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diff --git a/config.py b/config.py
new file mode 100644
index 00000000..06f083bc
--- /dev/null
+++ b/config.py
@@ -0,0 +1,22 @@
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+class Settings:
+    # database
+    DATABASE_URL: str = os.getenv("DATABASE_URL")
+
+    # Model
+    MODEL_PATH: str = os.getenv("MODEL_PATH")
+    MODEL_NAME: str = "RestNet50"
+
+    # API
+    HOST: str = os.getenv("HOST")
+    PORT: int = int(os.getenv("PORT"))
+    CORS_ORIGINS: str = os.getenv("CORS_ORIGINS")
+
+
+settings = Settings()
+
+
diff --git a/db/__pycache__/database.cpython-311.pyc b/db/__pycache__/database.cpython-311.pyc
index 858484e9156efdcec8e7cd78e6a492f51cd4dc18..ddcbaf25c0da29ecb8c75b0fe6b6db35089430e6 100644
GIT binary patch
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diff --git a/db/__pycache__/models.cpython-311.pyc b/db/__pycache__/models.cpython-311.pyc
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diff --git a/db/database.py b/db/database.py
index 06e93aa6..81e22682 100644
--- a/db/database.py
+++ b/db/database.py
@@ -1,12 +1,10 @@
 from sqlalchemy import create_engine
 from sqlalchemy.orm import sessionmaker
 from sqlalchemy.ext.declarative import declarative_base
-
-
-URL_DATABASE = 'postgresql://postgres:123456@localhost:5432/ai_project_database'
-engine = create_engine(URL_DATABASE)
-
+from config import settings
+ 
+ 
+engine = create_engine(settings.DATABASE_URL)
 SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 Base = declarative_base()
 
-
diff --git a/main.py b/main.py
new file mode 100644
index 00000000..fab191c2
--- /dev/null
+++ b/main.py
@@ -0,0 +1,51 @@
+from fastapi import FastAPI
+from contextlib import asynccontextmanager
+import uvicorn
+from fastapi.middleware.cors import CORSMiddleware
+
+from config import settings
+from db import models
+from db.database import engine
+from routes import foodsnap
+from ml_model.model import food_ai_model
+
+# Global model storage
+ml_models = {}
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):   
+    """ Calling Model only Single time"""
+
+    print(f'\nLoading {settings.MODEL_NAME} model')
+    ml_models["food_classifier"] = food_ai_model() # code to execute when app is loading
+    # expose models on app state for access within routes
+    app.state.ml_models = ml_models  
+    yield    
+    print('\nShutting down the model...')  # code to execute when app is shutting down
+
+#FastAPI app
+app = FastAPI(
+    title="FoodSnap AI",
+    description="AI-powered food classification and nutrition analysis",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+# Create database tables
+models.Base.metadata.create_all(bind=engine)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins= settings.CORS_ORIGINS,
+    allow_credentials=True,  # Allow cookies and authorization headers
+    allow_methods=["*"],     # Allow all HTTP methods (GET, POST, PUT, DELETE, etc.)
+    allow_headers=["*"],     # Allow all headers in the request
+)
+
+# router
+app.include_router(foodsnap.router)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host=settings.HOST, port=settings.PORT)
+
+
diff --git a/ml_model/__pycache__/model.cpython-311.pyc b/ml_model/__pycache__/model.cpython-311.pyc
index 0dda09db622b840f18aef980e6cfb38488532ca3..9b95ae4693760c7ec49a749cc9a92243d3efcf8c 100644
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diff --git a/ml_model/model.py b/ml_model/model.py
index 6c875a55..c354fdbd 100644
--- a/ml_model/model.py
+++ b/ml_model/model.py
@@ -1,6 +1,7 @@
 import tensorflow as tf 
 import cv2 as cv
 import numpy as np
+from config import settings
 
 
 def food_ai_model():
@@ -17,7 +18,7 @@ def food_ai_model():
                 and returns the predicted food name (string).
     """
 
-    model = tf.keras.models.load_model(r'c:\Users\GAFAR\Downloads\food_model.keras')
+    model = tf.keras.models.load_model(settings.MODEL_PATH)
 
     def predict_food_name(image):
         # image=cv.imread(image, cv.IMREAD_COLOR_RGB)
@@ -27,7 +28,7 @@ def predict_food_name(image):
 
         pred = model.predict(image)
 
-        class_names = ['adhirasam', 'aloo matar', 'aloo shimla mirch', 'aloo tikki', 'anarsa', 'apple fruit', 'ariselu',
+        class_names =  ['adhirasam', 'aloo matar', 'aloo shimla mirch', 'aloo tikki', 'anarsa', 'apple fruit', 'ariselu',
                         'banana fruit', 'bandar laddu', 'basundi', 'bhatura', 'bhindi masala', 'biryani', 'boiled egg',
                         'boiled rice', 'boondi raita', 'butter chicken', 'carrot', 'cauliflower', 'cham cham',
                         'chana masala', 'chapati', 'cheese pizza', 'chicken stew', 'chicken tikka', 'chicken tikka masala',
diff --git a/requirements.txt b/requirements.txt
index 60003ffb..8afe7205 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,4 +4,5 @@ tensorflow==2.19.0
 opencv-python==4.11.0.86
 beautifulsoup4==4.13.4
 SQLAlchemy==2.0.41
-psycopg2==2.9.10
\ No newline at end of file
+psycopg2==2.9.10
+python-dotenv==1.1.1
\ No newline at end of file
diff --git a/routes/__pycache__/foodsnap.cpython-311.pyc b/routes/__pycache__/foodsnap.cpython-311.pyc
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diff --git a/routes/foodsnap.py b/routes/foodsnap.py
new file mode 100644
index 00000000..6b7f266a
--- /dev/null
+++ b/routes/foodsnap.py
@@ -0,0 +1,83 @@
+from fastapi import APIRouter, UploadFile, HTTPException, Depends, Form, Request
+from starlette import status
+from typing import Annotated
+from sqlalchemy.orm import Session
+
+from db.database import SessionLocal
+from services.nutrition_service import NutritionService
+
+
+router = APIRouter(prefix="/foodsnap/v1", tags=["foodsnap"])
+
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()
+
+db_dependency = Annotated[Session, Depends(get_db)]
+
+def get_ml_models(request: Request) -> dict:
+    return request.app.state.ml_models
+
+
+@router.get('/', status_code= status.HTTP_200_OK)
+async def  project():
+    return ({"message": "Foodsnap Project"})
+
+@router.post('/upload', status_code=status.HTTP_200_OK)
+async def upload_food_image(db: db_dependency, image: UploadFile, 
+                            quantity: int = Form(1, gt=0),
+                            ml_models: dict = Depends(get_ml_models)):
+    """Upload and classify food image, return nutritional information"""
+
+    uploaded_image = await image.read()
+
+    try:
+        food_name = ml_models["food_classifier"](uploaded_image)
+        if not food_name:
+            raise ValueError('Food classification failed or returned empty result.')
+        
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        raise HTTPException(
+            status_code=500, 
+            detail='Could not classify the food item. Please try again or with a clearer image'
+        )
+
+    food_data = NutritionService.get_food_nutrition_data(db, food_name)
+    print(f"Food name --> {food_name}")
+    if not food_data:
+        raise HTTPException(
+            status_code=404, 
+            detail=f"Nutritional data for '{food_name}' could not be found."
+        )    
+
+    # Calculate nutritional values
+    calories = await NutritionService.calculate_nutritional_values(
+        quantity, float(food_data.calories)
+    )
+    carbohydrates = await NutritionService.calculate_nutritional_values(
+        quantity, float(food_data.carbohydrates)
+    )
+    protein = await NutritionService.calculate_nutritional_values(
+        quantity, float(food_data.protein)
+    )
+    fats = await NutritionService.calculate_nutritional_values(
+        quantity, float(food_data.fats)
+    )
+
+    nutrition_response = {
+            "Dish_Name": food_name,
+            "Quantity": quantity,
+            "Calories": f'{calories}',
+            "Carbohydrates": f'{carbohydrates}(g)',
+            "Protein": f'{protein}(g)',
+            "Fats": f'{fats}(g)'
+        }
+    
+    print(f"Response: {nutrition_response}")
+    return nutrition_response
+
+
diff --git a/script.py b/script.py
deleted file mode 100644
index 08313a13..00000000
--- a/script.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from fastapi import FastAPI, UploadFile, HTTPException, Depends, Query, Request
-from starlette import status
-from ml_model.model import food_ai_model
-from typing import Annotated
-from contextlib import asynccontextmanager
-
-from db import models
-from db.database import engine, SessionLocal
-from sqlalchemy.orm import Session
-from starlette.middleware.base import BaseHTTPMiddleware
-import time
-import logging
-
-
-ml_models = {}
-MODEL = 'RestNet50'
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):   
-    """ Calling Model only Single time"""
-
-    print(f'Loading {MODEL} model')
-    ml_models["food_classifier"] = food_ai_model() # code to execute when app is loading
-    yield    
-    print('Shutting down the model...')  # code to execute when app is shutting down
-
-app = FastAPI(lifespan=lifespan)
-models.Base.metadata.create_all(bind=engine)   # CREATE TABLES
-
-# DB CALL
-def get_db():
-    db = SessionLocal()
-    try:
-        yield db
-    finally:
-        db.close()
-db_dependency = Annotated[Session, Depends(get_db)] #DEPENDENCY INJECTION
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-class LoggingMiddleware(BaseHTTPMiddleware):
-    async def dispatch(self, request: Request, call_next):
-        start_time = time.time()
-        
-        # Log incoming request
-        logger.info(f"➡️ {request.method} {request.url.path}")        
-        response = await call_next(request)        
-        process_time = round((time.time() - start_time) * 1000, 2)
-        
-        # Log response status and duration
-        logger.info(f"⬅️ {request.method} {request.url.path} - {response.status_code} ({process_time} ms)")        
-        return response
-
-app.add_middleware(LoggingMiddleware)
-
-
-
-# ENDPOINTS
-@app.get('/')
-async def project():
-    return ({"message": "Food snap Project"})
-
-
-async def calculate_nutritional_values(quantity, macronutrients):
-    """Calculates the nutritional value based on quantity and macronutrients"""
-
-    value = ((macronutrients * 100) * quantity)/100
-    return value
-
-
-@app.post('/foodsnap/v1/upload', status_code= status.HTTP_200_OK)
-async def fetch_data(db: db_dependency,image: UploadFile, quantity: int = Query(gt=0)):  
-    """ 
-    Uploads a food image, classifies it using an ML model, retrieves its nutritional information from the database,
-    and calculates the total nutritional values based on the provided quantity.
-    
-    Returns:
-    A dictionary with the food's name, quantity, and calculated calories, carbs, protein, and fats.
-    """
-
-    uploaded_image = await image.read()
-    
-
-    try:
-        food_name = ml_models["food_classifier"](uploaded_image)
-        if not food_name:
-            raise ValueError('Food classification failed or returned empty result.')
-    except Exception as e:
-        print(f"An unexpected error occurred: {e}")
-        return HTTPException(status_code=500, detail='Could not classify the food item. Please try again or with a clearer image')
-    
-    food_quantity = quantity
-
-    food_data = db.query(models.CalorieNutritionData).filter(models.CalorieNutritionData.dish_name == food_name).first()
-    
-    if food_data:
-        calories = float(food_data.calories)
-        carbohydrates = float(food_data.carbohydrates)
-        protein = float(food_data.protein)
-        fats = float(food_data.fats)
-
-        result = {
-            "Dish Name": food_name,
-            "Quantity": food_quantity,
-            "Calories": f'{await calculate_nutritional_values(food_quantity, calories)}(g)',
-            "Carbohydrates" : f'{await calculate_nutritional_values(food_quantity, carbohydrates)}(g)',
-            "Protein" : f'{await calculate_nutritional_values(food_quantity, protein)}(g)',
-            "Fats" : f'{await calculate_nutritional_values(food_quantity, fats)}(g)' 
-        }
-        return result    
-    else:
-        return HTTPException(status_code=404, detail= f"Nutritional data for '{food_name}' could not be found.")
-
-
-
-
-
diff --git a/db/data-processing.py b/scripts/populate_database.py
similarity index 67%
rename from db/data-processing.py
rename to scripts/populate_database.py
index abe25809..5fdc6a2d 100644
--- a/db/data-processing.py
+++ b/scripts/populate_database.py
@@ -4,7 +4,6 @@
 from io import StringIO
 
 
-
 def psql_insert_copy(table, conn, keys, data_iter):
     """
     Execute SQL statement inserting data
@@ -35,16 +34,22 @@ def psql_insert_copy(table, conn, keys, data_iter):
             table_name, columns)
         cur.copy_expert(sql=sql, file=s_buf)
 
-df = pd.read_csv(r'C:\Users\GAFAR\Desktop\ai-project\web_scrap\cal_data.csv')
 
-df.to_sql(
-    name= 'nutrition_data_project',
-    con= engine,
-    if_exists= 'append',
-    index= False,
-    method= psql_insert_copy
-)
+def populate_database(csv_path):
+    """Populate database with nutrition data"""
+    df = pd.read_csv(csv_path)
+
+    df.to_sql(
+        name= 'nutrition_data_project',
+        con= engine,
+        if_exists= 'append',
+        index= False,
+        method= psql_insert_copy
+    )
+
+    print("DATA SAVED INTO TABLE SUCCESSFULLY !!!")
 
-print("DATA SAVED INTO TABLE SUCCESSFULLY !!!")
+# FASTEST METHOD TO STORE LARGE DATASETS INTO DB.
+if __name__ == "__main__":
+    populate_database(r'C:\Users\GAFAR\Desktop\ai-project\web_scrap\cal_data.csv')
 
-# FASTEST METHOD TO STORE LARGE DATASET INTO DB.
\ No newline at end of file
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diff --git a/services/nutrition_service.py b/services/nutrition_service.py
new file mode 100644
index 00000000..53e98629
--- /dev/null
+++ b/services/nutrition_service.py
@@ -0,0 +1,21 @@
+from db import models
+from sqlalchemy.orm import Session
+# from typing import Dict, Any
+
+
+class NutritionService:
+    @staticmethod
+    async def calculate_nutritional_values(quantity: int, macronutrients: float) -> float:
+        """Calculates the nutritional value based on quantity and macronutrients"""
+        value = ((macronutrients * 100) * quantity)/100
+        return value
+
+    @staticmethod
+    def get_food_nutrition_data(db: Session, food_name: str):
+        """Retrieve nutrition data for a food item"""
+
+        nutri_values = db.query(models.CalorieNutritionData).filter(models.CalorieNutritionData.dish_name == food_name).first()
+        return nutri_values
+
+
+