Create app.py

app.py

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+import base64
+import os
+import io
+
+import gradio as gr
+import requests
+from PIL import Image
+
+api_host = os.getenv('API_HOST', 'http://localhost:8005')
+
+
+def assess_image(img):
+    pil_image = Image.fromarray(img.astype('uint8'), 'RGB')
+    byte_arr = io.BytesIO()
+    pil_image.save(byte_arr, format='PNG')
+
+    gpt4o_url = f'{api_host}/v1/qc/gpt4o'
+    gemini_pro_url = f'{api_host}/v1/qc/gemini-pro'
+
+    request1 = requests.post(gpt4o_url, files={'file': byte_arr.getvalue()})
+    request2 = requests.post(gemini_pro_url, files={'file': byte_arr.getvalue()})
+
+    return dict(request1.json()), dict(request2.json())
+
+
+def assess_colour_accuracy(img1, img2):
+    pil_img1 = Image.fromarray(img1.astype('uint8'), 'RGB')
+    pil_img2 = Image.fromarray(img2.astype('uint8'), 'RGB')
+
+    byte_arr1 = io.BytesIO()
+    byte_arr2 = io.BytesIO()
+
+    pil_img1.save(byte_arr1, format='PNG')
+    pil_img2.save(byte_arr2, format='PNG')
+
+    qc_color_url = f'{api_host}/v1/qc/garment-color'
+
+    response = requests.post(qc_color_url, files={'file_1': byte_arr1.getvalue(), 'file_2': byte_arr2.getvalue()})
+
+    return str(response.text)
+
+
+iface1 = gr.Interface(fn=assess_image, inputs="image", outputs=[gr.JSON(label='GPT4o'), gr.JSON(label='Gemini Pro 1.5')])
+iface2 = gr.Interface(fn=assess_colour_accuracy,
+                      inputs=[gr.Image(label='Image'), gr.Image(label='Reference image')],
+                      outputs=gr.Textbox(label='Response'))
+
+demo = gr.TabbedInterface([iface1, iface2], ["Image Quality Assessment", "Colour Match Assessment"])
+demo.launch()
+