diff --git a/Neural Networks/Vehicle Classifier/README.md b/Neural Networks/Vehicle Classifier/README.md
new file mode 100644
index 00000000..cff7e31a
--- /dev/null
+++ b/Neural Networks/Vehicle Classifier/README.md
@@ -0,0 +1,58 @@
+# Vehicle Classifier AI
+
+## Description
+
+- Developed a high-performance **Vehicle Image Recognizer** web app using **deep learning techniques**.
+- Designed and implemented a **Convolutional Neural Network (CNN)** with **TensorFlow** and **Keras** to classify various vehicle types.
+- Integrated a **Flask** server for backend functionality, allowing users to upload vehicle images and receive real-time predictions.
+- Supports recognition of vehicles including **bus, family sedan, fire engine, heavy truck, jeep, minibus, racing car, SUV, taxi, and truck**.
+
+## Features
+
+- **Real-Time Predictions:** Utilized Flask to manage image uploads and provide on-the-fly predictions for the type of vehicle.
+- **Interactive Web App:** Designed an intuitive web interface where users can easily upload images and get immediate classification results.
+- **Highly Accurate Model:** Trained the CNN on a dataset of 1,400 images for training and 200 images for validation, leading to a highly performant model.
+
+## Tech Stack
+
+- **Programming Language:** Python
+- **Machine Learning Libraries:** TensorFlow, Keras
+- **Web Framework:** Flask
+- **Frontend Technologies:** HTML5, CSS3, JavaScript
+- **Dataset:**
+ - Vehicle classification dataset from Kaggle: [Kaggle Dataset](https://www.kaggle.com/datasets/marquis03/vehicle-classification)
+
+## Installation Instructions
+
+`Run all the cells in jupyter, you will have vehicle_model.h5. Now follow the instructions`
+
+1. Clone the GitHub repository:
+ ```bash
+ git clone https://github.com/UppuluriKalyani/ML-Nexus.git
+ ```
+
+2. Navigate to the project directory:
+ ```bash
+ cd Neural Networks
+ cd Vehicle Classifier
+ ```
+
+3. Install the required Python packages:
+ ```bash
+ pip install -r requirements.txt
+ ```
+
+4. Run the Flask app:
+ ```bash
+ python app.py
+ ```
+
+5. Open your web browser and go to `http://127.0.0.1:5000` to use the app.
+
+## Screenshot
+
+
+
+## Demo Video
+
+https://github.com/user-attachments/assets/c9c5d5f9-ba2c-4969-8685-beed10b1e39a
diff --git a/Neural Networks/Vehicle Classifier/app.py b/Neural Networks/Vehicle Classifier/app.py
new file mode 100644
index 00000000..001e3db4
--- /dev/null
+++ b/Neural Networks/Vehicle Classifier/app.py
@@ -0,0 +1,62 @@
+from flask import Flask, render_template, request, redirect, url_for
+from tensorflow.keras.preprocessing.image import ImageDataGenerator # type: ignore
+from tensorflow.keras.models import load_model # type: ignore
+from tensorflow.keras.preprocessing import image # type: ignore
+import tensorflow as tf
+import numpy as np
+import os
+
+app = Flask(__name__)
+
+train_dir = 'dataset/train'
+
+model = load_model('vehicle_model.h5')
+
+batch_size = 32
+
+train_datagen = ImageDataGenerator(rescale=1/255)
+
+classes =['bus', 'family sedan', 'fire engine', 'heavy truck', 'jeep', 'minibus', 'racing car', 'SUV', 'taxi', 'truck']
+
+train_generator = train_datagen.flow_from_directory(
+ train_dir,
+ target_size=(600, 600),
+ batch_size=batch_size,
+ classes = classes,
+ class_mode='categorical'
+)
+
+loaded_model = tf.keras.models.load_model('vehicle_model.h5')
+
+def predict_vehicle_with_loaded_model(img_path):
+ img = image.load_img(img_path, target_size=(600, 600))
+ img_array = image.img_to_array(img)
+ img_array = np.expand_dims(img_array, axis=0)
+ img_array /= 255.0
+
+ predictions = loaded_model.predict(img_array)
+ class_idx = np.argmax(predictions[0])
+ class_label = list(train_generator.class_indices.keys())[class_idx]
+ return class_label
+
+@app.route('/', methods=['GET', 'POST'])
+def upload_file():
+ if request.method == 'POST':
+ print("Received POST request")
+ if 'file' not in request.files:
+ print("No file part in request")
+ return redirect(request.url)
+ file = request.files['file']
+ if file.filename == '':
+ print("No selected file")
+ return redirect(request.url)
+ if file:
+ file_path = os.path.join('static', file.filename)
+ print(f"Saving file to {file_path}")
+ file.save(file_path)
+ label = predict_vehicle_with_loaded_model(file_path)
+ return render_template('app.html', label=label, file_path=file.filename)
+ return render_template('app.html')
+
+if __name__ == '__main__':
+ app.run(debug=True)
\ No newline at end of file
diff --git a/Neural Networks/Vehicle Classifier/requirements.txt b/Neural Networks/Vehicle Classifier/requirements.txt
new file mode 100644
index 00000000..84f8da0e
--- /dev/null
+++ b/Neural Networks/Vehicle Classifier/requirements.txt
@@ -0,0 +1,4 @@
+Flask
+tensorflow
+numpy
+keras
\ No newline at end of file
diff --git a/Neural Networks/Vehicle Classifier/static/css/styles.css b/Neural Networks/Vehicle Classifier/static/css/styles.css
new file mode 100644
index 00000000..5aec495c
--- /dev/null
+++ b/Neural Networks/Vehicle Classifier/static/css/styles.css
@@ -0,0 +1,277 @@
+* {
+ margin: 0;
+ padding: 0;
+ box-sizing: border-box;
+ font-family: 'Poppins', sans-serif;
+}
+
+:root {
+ --blue: #0071FF;
+ --light-blue: #B6DBF6;
+ --dark-blue: #005DD1;
+ --grey: #f2f2f2;
+}
+
+body {
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ min-height: 100vh;
+ background: var(--light-blue);
+}
+
+.container {
+ max-width: 400px;
+ width: 100%;
+ background: #fff;
+ padding: 30px;
+ border-radius: 30px;
+ margin-top:30px;
+ margin-bottom: 30px;
+}
+
+header {
+ position: fixed;
+ top: 0;
+ left: 0;
+ width: 100%;
+ padding: 20px 80px;
+ display: flex;
+ background: black;
+ justify-content: space-between;
+ align-items: center;
+ z-index: 99;
+ font-size: 13px;
+}
+
+.title {
+ position: relative;
+ width: 100%;
+ height: 60px;
+ background: rgb(226, 220, 220);
+ margin-bottom: 20px;
+ border-radius: 15px;
+ overflow: hidden;
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ flex-direction: column;
+ font-size: 25px;
+ font-weight: 700;
+}
+
+.img-area {
+ position: relative;
+ width: 100%;
+ height: 300px;
+ background: var(--grey);
+ margin-bottom: 30px;
+ border-radius: 15px;
+ overflow: hidden;
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ flex-direction: column;
+}
+
+.img-area .icon {
+ font-size: 100px;
+}
+
+.img-area h3 {
+ font-size: 20px;
+ font-weight: 500;
+ margin-bottom: 6px;
+}
+
+.img-area p {
+ color: #999;
+}
+
+.img-area img {
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ height: 100%;
+ object-fit: cover;
+ object-position: center;
+ z-index: 100;
+ display: none;
+}
+
+.img-area.show-img img {
+ display: block;
+}
+.img-area::before {
+ content: attr(data-img);
+ position: absolute;
+ top: 0;
+ left: 0;
+ width: 100%;
+ height: 100%;
+ background: rgba(0, 0, 0, .5);
+ color: #fff;
+ font-weight: 500;
+ text-align: center;
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ pointer-events: none;
+ opacity: 0;
+ transition: all .3s ease;
+ z-index: 200;
+}
+
+.img-area.active:hover::before {
+ opacity: 1;
+}
+
+.select-image,
+.submit-image,
+.upload-another {
+ display: block;
+ width: 100%;
+ padding: 16px 0;
+ border-radius: 15px;
+ background: var(--blue);
+ color: #fff;
+ font-weight: 500;
+ font-size: 16px;
+ border: none;
+ cursor: pointer;
+ transition: all .3s ease;
+ margin-top: 15px;
+ margin-bottom: 10px;
+ text-align: center;
+}
+
+.select-image:hover,
+.submit-image:hover,
+.upload-another:hover {
+ background: var(--dark-blue);
+}
+
+.submit-image {
+ margin-top: 10px;
+}
+
+.navigation {
+ display: flex;
+ justify-content: space-between;
+ align-items: center;
+}
+
+.navigation a {
+ position: relative;
+ font-size: 1.1em;
+ color: #fff;
+ text-decoration: none;
+ font-weight: 500;
+ margin-left: 40px;
+}
+
+.navigation a::after {
+ content: '';
+ position: absolute;
+ left: 0;
+ bottom: -6px;
+ width: 100%;
+ height: 3px;
+ background: #fff;
+ border-radius: 5px;
+ transform-origin: right;
+ transform: scaleX(0);
+ transition: transform .5s;
+}
+
+.navigation a:hover::after {
+ transform-origin: left;
+ transform: scaleX(1);
+}
+
+.logo {
+ font-size: 2em;
+ color: #fff;
+ user-select: none;
+}
+
+.menu-toggle {
+ display: none;
+ color: #fff;
+ font-size: 2em;
+ cursor: pointer;
+}
+
+.image_display {
+ margin-bottom: 20px;
+}
+
+img {
+ max-width: 100%;
+ height: auto;
+ border-radius: 8px;
+}
+
+.info {
+ position: relative;
+ overflow: hidden;
+ display: flex;
+ justify-content: center;
+ align-items: center;
+ flex-direction: column;
+ margin-top: 100px;
+ font-size: 25px;
+ font-weight: 700;
+ font-size: 1.5rem; /* Adjust font size as needed */
+ text-align: center;
+ padding: 0 20px; /* Adds space on both sides */
+}
+
+
+@media (max-width: 768px) {
+ header {
+ padding: 20px;
+ }
+
+ .navigation {
+ display: none;
+ flex-direction: column;
+ background: black;
+ width: 100%;
+ position: absolute;
+ top: 60px;
+ left: 0;
+ }
+
+ .navigation.active {
+ display: flex;
+ }
+
+ .navigation a {
+ margin: 10px 0;
+ }
+
+ .menu-toggle {
+ display: block;
+
+ }
+
+ .wrapper {
+ width: 90%;
+ max-width: 100%;
+ height: auto;
+ margin-top: 70px;
+ margin-bottom: 10px;
+ }
+
+ .info {
+ padding: 0 15px;
+ }
+}
+
+@media (max-width: 480px) {
+ .info {
+ padding: 0 10px; /* Further adjust padding for smaller screens */
+ }
+}
\ No newline at end of file
diff --git a/Neural Networks/Vehicle Classifier/templates/app.html b/Neural Networks/Vehicle Classifier/templates/app.html
new file mode 100644
index 00000000..74a32557
--- /dev/null
+++ b/Neural Networks/Vehicle Classifier/templates/app.html
@@ -0,0 +1,83 @@
+
+
+
+
+
+ Vehicle Recognizer
+
+
+
+
+
+
+
+
+
+
+
+
+
+
A Deep Learning Model Developed Using Convolutional Neural Networks (CNN)
+
+
Vechicle Recognizer AI
+ {% if file_path %}
+
+
This is {{ label }}
+
 }})
+
+
+ {% else %}
+
+ {% endif %}
+
+