Upload app.py

app.py

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+import numpy as np
+
+from pyrolens_deployment.gradio_app.dehazing_gen import CycleGenerator
+
+import torch
+from torchvision import transforms
+
+import gradio as gr
+
+gan = CycleGenerator(num_residuals=6)
+gan.load_state_dict(torch.load("genC.pth.tar", map_location=torch.device('cpu')))
+
+
+def dehaze(img):
+    gan_transforms = transforms.Compose([
+        transforms.Resize((800, 800)),
+        transforms.ToTensor()
+    ])
+    dehazed_output = gan(gan_transforms(img))
+    out_arr = dehazed_output.detach().cpu()
+    return np.array(out_arr).transpose(1, 2, 0)
+
+
+sample_images = [
+    ("Haze", "gradio_check1.png"),
+    ("Haze", "gradio_check10.png"),
+    ("Haze", "gradio_check13.png"),
+]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# ClarityGAN")
+    gr.Markdown("## Image Dehazing using CycleGANs")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image", type="pil")
+            with gr.Row():
+                dehaze_button = gr.Button("Dehaze")
+        with gr.Column():
+            output_image = gr.Image(label="Output Image", type="pil")
+            for name, file in sample_images:
+                gr.Button(name).click(dehaze, inputs=input_image, outputs=output_image)
+
+demo.launch()