diff --git a/app.py b/app.py
new file mode 100644
index 0000000..ad102ec
--- /dev/null
+++ b/app.py
@@ -0,0 +1,65 @@
+from flask import Flask, request, render_template, jsonify
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+import os
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__)
+
+# Load the pre-trained model
+model = tf.keras.models.load_model("cheat_detector_model.h5")
+
+# Define the path to save uploaded images
+UPLOAD_FOLDER = "static/uploads"
+app.config["UPLOAD_FOLDER"] = UPLOAD_FOLDER
+
+# Ensure the upload folder exists
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+
+# Preprocess the image for prediction
+def prepare_image(image_path):
+ img = load_img(
+ image_path, target_size=(150, 150)
+ ) # Resize as per model input shape
+ img_array = img_to_array(img)
+ img_array = np.expand_dims(img_array, axis=0)
+ img_array /= 255.0 # Normalize if required by the model
+ return img_array
+
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+ if request.method == "POST":
+ if "file" not in request.files:
+ return "No file part"
+ file = request.files["file"]
+ if file.filename == "":
+ return "No selected file"
+ if file:
+ filename = secure_filename(file.filename)
+ filepath = os.path.join(app.config["UPLOAD_FOLDER"], filename)
+ file.save(filepath)
+
+ # Preprocess the image and make prediction
+ img_array = prepare_image(filepath)
+ prediction = model.predict(img_array)[0][0]
+
+ # Classify as "Cheating" or "Not Cheating"
+ if prediction > 0.5: # Adjust threshold as needed
+ result = "Cheating"
+ else:
+ result = "Not Cheating"
+
+ # Remove the saved image after prediction
+ os.remove(filepath)
+
+ # Return result as JSON
+ return jsonify({"result": result, "confidence": float(prediction)})
+
+ return render_template("index.html")
+
+
+if __name__ == "__main__":
+ app.run(debug=True)
diff --git a/cheat_detector_modelh5.py b/cheat_detector_modelh5.py
new file mode 100644
index 0000000..0b40b27
--- /dev/null
+++ b/cheat_detector_modelh5.py
@@ -0,0 +1,29 @@
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+# Define a simple CNN model
+model = Sequential([
+ Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
+ MaxPooling2D(2, 2),
+ Conv2D(64, (3, 3), activation='relu'),
+ MaxPooling2D(2, 2),
+ Flatten(),
+ Dense(128, activation='relu'),
+ Dense(1, activation='sigmoid') # Sigmoid for binary classification
+])
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Training data generators
+train_datagen = ImageDataGenerator(rescale=1./255)
+train_generator = train_datagen.flow_from_directory(
+ 'data/train', # Training data directory
+ target_size=(150, 150),
+ batch_size=32,
+ class_mode='binary'
+)
+
+# Fit the model
+model.fit(train_generator, epochs=10)
+model.save('cheat_detector_model.h5')
diff --git a/static/styles.css b/static/styles.css
new file mode 100644
index 0000000..f4f6a91
--- /dev/null
+++ b/static/styles.css
@@ -0,0 +1,19 @@
+body {
+ font-family: Arial, sans-serif;
+ text-align: center;
+ margin: 0;
+ padding: 0;
+}
+
+h2 {
+ margin-top: 20px;
+}
+
+#upload-form {
+ margin-top: 30px;
+}
+
+#result {
+ margin-top: 20px;
+ font-weight: bold;
+}
\ No newline at end of file
diff --git a/templates/Imagesprocessing.html b/templates/Imagesprocessing.html
new file mode 100644
index 0000000..df208f8
--- /dev/null
+++ b/templates/Imagesprocessing.html
@@ -0,0 +1,19 @@
+
+
+
+
+
+ Cheating Detection
+
+
+ Upload an Image to Check for Cheating
+
+ {% if result %}
+ Prediction: {{ result }}
+ Confidence: {{ confidence }}
+ {% endif %}
+
+