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| import os | |
| import sys | |
| import json | |
| import requests | |
| import gradio as gr | |
| from huggingface_hub import HfFileSystem, hf_hub_download | |
| from PIL import Image | |
| # Environment Variables | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| HF_DATASET = os.getenv('HF_DATASET') | |
| SPACE_SUBDOMAIN = os.environ['SPACE_SUBDOMAIN'] | |
| def get_image_with_auth(file_name): | |
| """Retrieve an image using Hugging Face's hub with authentication.""" | |
| image_path = hf_hub_download(repo_id=HF_DATASET, repo_type="dataset", filename=file_name, token=HF_TOKEN) | |
| return Image.open(image_path) | |
| def recognize_face(image): | |
| """ | |
| Function to send either an image URL to the FastAPI backend and receive results. | |
| """ | |
| # Set the URL to your FastAPI endpoint | |
| url = 'https://dwancin-face-match-api.hf.space/recognize/' | |
| # Prepare the payload with the image data and specify the type | |
| payload = { | |
| "image": f"https://{SPACE_SUBDOMAIN}.hf.space/file={image}", | |
| "type": "url" | |
| } | |
| # Prepare the headers with the Authorization token | |
| headers = { | |
| "Authorization": f"Bearer {HF_TOKEN}" | |
| } | |
| # Send POST request to FastAPI server with the image data and type | |
| response = requests.post(url, json=payload, headers=headers) | |
| # Process response | |
| if response.status_code == 200: | |
| response_data = response.json() | |
| image_path = response_data.get('image') | |
| if image_path: | |
| image_file = get_image_with_auth(image_path) | |
| formatted_json = json.dumps(response_data, indent=4) | |
| info = f"```json\n{formatted_json}\n```" | |
| print(formatted_json) | |
| return image_file, info | |
| else: | |
| info = "No image path found in response." | |
| print(info) | |
| return None, info | |
| else: | |
| info = f"Error: {response.status_code} - {response.text}" | |
| print(info) | |
| return None, f"Error: {response.status_code} - {response.text}" | |
| def update(output_info): | |
| return gr.update(visible=True) | |
| # Gradio setup | |
| with gr.Blocks( | |
| analytics_enabled=False, | |
| title="Face Match", | |
| css=''' | |
| .gradio-container { max-width: 700px !important; } | |
| .source-selection { display: none !important; } | |
| #clear { max-width: 140px; } | |
| #submit { max-width: 240px; } | |
| .svelte-1pijsyv { border-radius: 0 !important; } | |
| .svelte-s6ybro { display: none !important; } | |
| ''' | |
| ) as demo: | |
| title = gr.HTML("<h1><center>Face Match</center></h1>") | |
| subtitle = gr.HTML("<h3><center>Upload an image, and the system will find the most similar face in our dataset.</center></h3>") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(equal_height=True): | |
| input_image = gr.Image(type="filepath", show_label=False, interactive=True) | |
| output_image = gr.Image(type="filepath", show_label=False, interactive=False, show_share_button=False, show_download_button=False) | |
| with gr.Row(): | |
| output_info = gr.Markdown(visible=False) | |
| with gr.Row(): | |
| clear = gr.ClearButton([input_image, output_image, output_info], elem_id="clear", elem_classes="button") | |
| submit = gr.Button("Submit", variant="primary", elem_id="submit", elem_classes="button") | |
| with gr.Row(): | |
| examples = gr.Examples(["examples/0001.png", "examples/0002.png", "examples/0003.png", "examples/0004.png"], input_image) | |
| output_image.change(fn=update, inputs=output_info, outputs=output_info) | |
| submit.click(fn=recognize_face, inputs=input_image, outputs=[output_image, output_info]) | |
| # Launch | |
| demo.launch(show_api=False) | |