Image Classification Application: Cheating vs. Not Cheating tanishane…

…ss#103
Ananya-vastare · Nov 9, 2024 · 3129c23 · 3129c23
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diff --git a/app.py b/app.py
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+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
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+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
@@ -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;
+}
diff --git a/templates/Imagesprocessing.html b/templates/Imagesprocessing.html
@@ -0,0 +1,19 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Cheating Detection</title>
+</head>
+<body>
+    <h2>Upload an Image to Check for Cheating</h2>
+    <form action="/" method="post" enctype="multipart/form-data">
+        <input type="file" name="file" accept="image/*" required>
+        <button type="submit">Upload and Detect</button>
+    </form>
+    {% if result %}
+        <h3>Prediction: {{ result }}</h3>
+        <p>Confidence: {{ confidence }}</p>
+    {% endif %}
+</body>
+</html>