Create app.py

app.py

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+import gradio as gr
+
+from PIL import Image
+import numpy as np
+import os
+from face_cropper import detect_and_label_faces
+# Define a custom function to convert an image to grayscale
+def to_grayscale(input_image):
+    grayscale_image = Image.fromarray(np.array(input_image).mean(axis=-1).astype(np.uint8))
+    return grayscale_image
+
+
+description_markdown = """
+# Fake Face Detection tool from TrustWorthy BiometraVision Lab IISER Bhopal
+## Usage
+This tool expects a face image as input. Upon submission, it will process the image and provide an output with bounding boxes drawn on the face. Alongside the visual markers, the tool will give a detection result indicating whether the face is fake or real.
+## Disclaimer
+Please note that this tool is for research purposes only and may not always be 100% accurate. Users are advised to exercise discretion and supervise the tool's usage accordingly.
+## Licensing and Permissions
+This tool has been developed solely for research and demonstrative purposes. Any commercial utilization of this tool is strictly prohibited unless explicit permission has been obtained from the developers.
+## Developer Contact
+For further inquiries or permissions, you can reach out to the developer through the following social media accounts:
+- [LAB Webpage](https://sites.google.com/iiitd.ac.in/agarwalakshay/labiiserb?authuser=0)
+- [LinkedIn](https://www.linkedin.com/in/shivam-shukla-0a50ab1a2/)
+- [GitHub](https://github.com/SaShukla090)
+"""
+
+
+
+
+# Create the Gradio app
+app = gr.Interface(
+    fn=detect_and_label_faces,
+    inputs=gr.Image(type="pil"),
+    outputs="image",
+    # examples=[
+    #     "path_to_example_image_1.jpg",
+    #     "path_to_example_image_2.jpg"
+    # ]
+    examples=[
+        os.path.join("Examples", image_name) for image_name in os.listdir("Examples")
+    ],
+    title="Fake Face Detection",
+    description=description_markdown,
+)
+
+# Run the app
+app.launch()