Create app.py

app.py

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+import gradio as gr
+from keras.preprocessing import image
+from keras.applications.vgg16 import preprocess_input
+import numpy as np
+from keras.models import load_model
+import cv2 as cv
+
+# Load the trained model
+model = load_model('fake_real_face_classification_model.keras')
+
+# Load the pre-trained face detection model with error handling
+face_cascade = cv.CascadeClassifier('img_for_deepfake_detection\\hass_face.xml')
+
+# Define a function to preprocess the input image
+def preprocess_image(image_path):
+    img = cv.imread(image_path)
+    img = cv.resize(img, (224, 224))
+    img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+    img_array = np.expand_dims(img, axis=0)
+    img_array = preprocess_input(img_array)
+    return img_array
+
+# Define a function to classify the input image
+def classify_image(image_path):
+    # Preprocess the image
+    img_array = preprocess_image(image_path)
+
+    # Convert the image to grayscale
+    gray_image = cv.cvtColor(cv.imread(image_path), cv.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Check if any faces were detected
+    if len(faces) == 0:
+        return "No faces detected in the input image."
+    else:
+        # Make predictions
+        prediction = model.predict(img_array)
+
+        # Return the prediction
+        if prediction[0][0] > 0.5:
+            return "The image is classified as real."
+        else:
+            return "The image is classified as fake."
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="file", label="Upload Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="DeepFake Image Detection",
+    description="Upload an image and the model will classify it as real or fake.",
+    theme="default",
+    layout="vertical",
+    css="""
+        .gradio-container {
+            font-family: 'Roboto', sans-serif;
+        }
+        .gradio-input, .gradio-output {
+            border: 1px solid #ccc;
+            border-radius: 4px;
+            padding: 10px;
+            font-size: 16px;
+        }
+        .gradio-button {
+            background-color: #4CAF50;
+            color: white;
+            border: none;
+            border-radius: 4px;
+            padding: 10px 20px;
+            font-size: 16px;
+            cursor: pointer;
+        }
+    """
+)
+
+# Launch the Gradio app
+demo.launch()