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| import os | |
| import gradio as gr | |
| from deepface import DeepFace | |
| # Path to the dataset folder | |
| DATASET_PATH = "face_dataset" | |
| # Load dataset images (only image files are included) | |
| dataset_images = { | |
| os.path.splitext(filename)[0]: os.path.join(DATASET_PATH, filename) # Remove file extension | |
| for filename in os.listdir(DATASET_PATH) | |
| if filename.lower().endswith(('.jpg', '.jpeg', '.png')) | |
| } | |
| def recognize_face(uploaded_image): | |
| """ | |
| Compare the uploaded image with dataset images using DeepFace. | |
| Returns the matched model name as an Instagram link and its image if a verified match is found. | |
| """ | |
| if uploaded_image is None: | |
| return "No image uploaded.", None | |
| # Loop through each image in the dataset | |
| for model_name, image_path in dataset_images.items(): | |
| try: | |
| result = DeepFace.verify( | |
| uploaded_image, | |
| image_path, | |
| model_name="VGG-Face", | |
| enforce_detection=False | |
| ) | |
| if result.get("verified"): | |
| # Create an Instagram link | |
| model_link = f'<a href="https://www.instagram.com/{model_name}/" target="_blank">{model_name}</a>' | |
| return model_link, image_path | |
| except Exception as e: | |
| print(f"Error processing {image_path}: {e}") | |
| return "No matching face found.", None | |
| # Create a Gradio interface for the app | |
| iface = gr.Interface( | |
| fn=recognize_face, | |
| inputs=gr.Image(type="numpy", label="Upload an Image"), | |
| outputs=[ | |
| gr.HTML(label="Model Name"), # Use HTML for clickable link | |
| gr.Image(label="Matched Model Image") | |
| ], | |
| title="Face Recognition App", | |
| description="Upload an image to find the most similar face from the dataset." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |