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app.py

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+import aiohttp
+import gradio as  gr
+import numba
+import requests 
+import  base64
+from PIL import Image
+import  io
+import json
+from numba import jit
+import matplotlib.pyplot as plt
+
+
+examples =  ["examples/0002_01_00_01_55.jpg",
+"examples/0-spoof.jpg",
+"examples/0.jpg",
+"examples/3.jpg",
+"examples/6-mask.jpg",
+"examples/AGL752VM_id147_s0_150.png",
+"examples/FT720P_G780_REDMI4X_id0_s0_105.png",
+"examples/7.jpg"]
+
+
+async def spoof_trigger(b64):
+    url = "https://spoofapi1.azurewebsites.net/api/spoofvisualize"
+    payload = {"img": b64}
+    headers = {
+    'x-functions-key': 'wGw3zXXPlLCez-VrcSs9RTahE4gLC674pf7Fp6Au2kUHAzFuNnZZMw==',
+    'Content-Type': 'text/plain'
+    }
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, json=payload, headers=headers) as response:
+            response_text = await response.text()
+    return response_text
+# @jit
+async def predict_image(img):
+    # Convert NumPy array to PIL Image
+    img = Image.fromarray(img.astype('uint8'))
+    
+    # Create a BytesIO object
+    buffer = io.BytesIO()
+    
+    # Save the PIL Image to the BytesIO object
+    img.save(buffer, format='JPEG')
+    
+    # Get the base64 representation
+    img_base64 = base64.b64encode(buffer.getvalue()).decode()
+
+    print(len(img_base64))
+    # # img_base64 to  plot
+    # img = Image.open(io.BytesIO(base64.b64decode(img_base64)))
+    # # img save 
+    # img.save("img.jpg")
+
+
+    res = await spoof_trigger(img_base64)
+    # print(json.loads(res))
+    spoof_res = json.loads(res)['spoof_res']
+    annotated_image  = json.loads(res)['annotated_image']
+    conf_score = float( json.loads(spoof_res)['confidence_score'])
+
+    # img_base64 to  plot
+    img = Image.open(io.BytesIO(base64.b64decode(annotated_image)))
+    # img save 
+    img.save("cache/img.jpg")
+
+    confidences = {'Real': 1-conf_score, 'Fake': conf_score}
+
+    return (confidences,annotated_image)
+
+
+with gr.Blocks(title="Spoof-Demo", css="#custom_header {min-height: 3rem; text-align:  center} #custom_title {min-height: 3rem; text-align: center}") as demo :
+    gr.Markdown("## Face Antispoof-Demo", elem_id="custom_title")
+    gr.Markdown("Gradio Demo for Face Antispoofing", elem_id="custom_header")
+    gr.Markdown("👨‍💻 Only fot research preview Intended")
+    with gr.Row():
+        with gr.Column():
+            with gr.Box():
+                gr.Markdown("### Input")
+                image = gr.Image(label="Input Image")
+                image.style(height=240) 
+                btn = gr.Button(text="Submit")
+                btn.style(full_width=True)          
+        with gr.Column():
+            with gr.Box():
+                gr.Markdown("### Output")
+                output_image = gr.Image(label="Output Image")
+                output_image.style(height=240) 
+                label_probs  =  gr.outputs.Label()
+
+    btn.click(predict_image, image , outputs=[label_probs,output_image ],api_name="Face Antispoofing")
+    gr.Examples(
+        examples=examples,
+        inputs=image,
+        outputs = output_image,   
+        fn=predict_image,
+        cache_examples=False,
+    ) 
+
+if  __name__ == "__main__":
+    demo.launch(debug=True)

test.py

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+import aiohttp
+import json 
+import base64
+
+async def spoof_trigger(b64):
+    url = "https://spoofapi1.azurewebsites.net/api/spoofvisualize"
+    payload = {"img": b64}
+    headers = {
+    'x-functions-key': 'wGw3zXXPlLCez-VrcSs9RTahE4gLC674pf7Fp6Au2kUHAzFuNnZZMw==',
+    'Content-Type': 'text/plain'
+    }
+
+    async with aiohttp.ClientSession() as session:
+        async with session.post(url, json=payload, headers=headers) as response:
+            response_text = await response.text()
+            spoof_res = json.loads(response_text)
+    return spoof_res
+
+a = input('input:')
+x = spoof_trigger(a)
+print(x)