diff --git a/Dataset/README.dataset.txt b/Dataset/README.dataset.txt
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+# 2500 person animal car > 7500 animal car person
+https://universe.roboflow.com/object-y2str/2500-person-animal-car
+
+Provided by a Roboflow user
+License: CC BY 4.0
+
diff --git a/Dataset/README.roboflow.txt b/Dataset/README.roboflow.txt
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+
+2500 person animal car - v1 7500 animal car person
+==============================
+
+This dataset was exported via roboflow.com on June 13, 2024 at 10:45 PM GMT
+
+Roboflow is an end-to-end computer vision platform that helps you
+* collaborate with your team on computer vision projects
+* collect & organize images
+* understand and search unstructured image data
+* annotate, and create datasets
+* export, train, and deploy computer vision models
+* use active learning to improve your dataset over time
+
+For state of the art Computer Vision training notebooks you can use with this dataset,
+visit https://github.com/roboflow/notebooks
+
+To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
+
+The dataset includes 7499 images.
+Person-animal-car are annotated in COCO format.
+
+The following pre-processing was applied to each image:
+* Auto-orientation of pixel data (with EXIF-orientation stripping)
+* Resize to 416x416 (Stretch)
+
+No image augmentation techniques were applied.
+
+
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\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..85ec13059e6ef6d1ce044d59f9175dc3b3390f01
--- /dev/null
+++ b/app.py
@@ -0,0 +1,93 @@
+import streamlit as st
+import numpy as np
+import json
+import os
+import cv2
+from sklearn.preprocessing import StandardScaler
+from sklearn.neighbors import KNeighborsClassifier
+
+# Load COCO JSON dataset
+def load_coco_dataset(json_file, image_dir):
+    with open(json_file, 'r') as f:
+        data = json.load(f)
+    
+    images, labels = [], []
+    label_dict = {}
+    
+    for annotation in data['annotations']:
+        image_id = annotation['image_id']
+        category_id = annotation['category_id']
+        
+        image_info = next((img for img in data['images'] if img['id'] == image_id), None)
+        if image_info is None:
+            continue
+        
+        image_path = os.path.join(image_dir, image_info['file_name'])
+        if not os.path.exists(image_path):
+            continue
+        
+        image = cv2.imread(image_path)
+        image = cv2.resize(image, (64, 64))
+        image = image / 255.0  # Normalize to [0,1]
+        
+        images.append(image)
+        labels.append(category_id)
+        
+        if category_id not in label_dict:
+            label_dict[category_id] = next(cat['name'] for cat in data['categories'] if cat['id'] == category_id)
+    
+    return np.array(images), np.array(labels), label_dict
+
+# Load dataset paths
+train_json_file = r"C:\Users\Ethan\Desktop\HuggingFace\Road_Classifier\Dataset\Train\_annotations.coco.json"
+train_image_directory = r"C:\Users\Ethan\Desktop\HuggingFace\Road_Classifier\Dataset\Train\images"
+
+test_json_file = r"C:\Users\Ethan\Desktop\HuggingFace\Road_Classifier\Dataset\Test\_annotations.coco.json"
+test_image_directory = r"C:\Users\Ethan\Desktop\HuggingFace\Road_Classifier\Dataset\Test\images"
+
+# Load datasets
+X_train, y_train, _ = load_coco_dataset(train_json_file, train_image_directory)
+X_test, y_test, label_dict = load_coco_dataset(test_json_file, test_image_directory)
+
+# Flatten images for KNN
+X_train = X_train.reshape(X_train.shape[0], -1)
+X_test = X_test.reshape(X_test.shape[0], -1)
+
+# Scale the data
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Train KNN model
+model = KNeighborsClassifier(n_neighbors=5)
+model.fit(X_train, y_train)
+
+# Streamlit UI
+st.set_page_config(page_title="Road Object Classifier", layout="wide")
+st.title("🚗🚶🐾 KNN Road Object Classifier App")
+
+# Tabs for UI
+tabs = st.tabs(["Classifier", "How It Works"])
+
+with tabs[0]:
+    st.header("Upload an Image for Classification")
+    uploaded_file = st.file_uploader("Upload an image (Car, People, or Animal)", type=["jpg", "png", "jpeg"])
+    if uploaded_file:
+        image = cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), cv2.IMREAD_COLOR)
+        image = cv2.resize(image, (64, 64))
+        image_display = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = image / 255.0
+        image = image.reshape(1, -1)
+        image = scaler.transform(image)
+        prediction = model.predict(image)[0]
+        predicted_label = label_dict.get(prediction, "Unknown")
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(image_display, caption="Uploaded Image", use_column_width=True)
+        with col2:
+            st.markdown(f"## **Predicted Object:** {predicted_label}")
+
+with tabs[1]:
+    st.header("How the App Works")
+    st.write("This app uses a K-Nearest Neighbors (KNN) model to classify objects on roads. The model is trained on a dataset labeled using COCO annotations and processes uploaded images to predict whether they contain a car, a person, or an animal.")