Create app.py

app.py

@@ -0,0 +1,34 @@
+import cv2
+import os
+import torch
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
+
+import gradio as gr
+import torchvision.transforms as transforms
+
+
+model_path = "Trained_ESRGAN.pth"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+upsampler = RealESRGANer(scale=4, model_path=model_path, model=model)
+
+
+
+def esrgan(input_image):
+  output_img, _ = upsampler.enhance(input_image, outscale=3.5)
+  filename = "output.jpg"
+  output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2RGB)
+  cv2.imwrite(filename, output_img)    
+  return filename
+
+
+# Define the Gradio app interface
+inputs = gr.Image(label="Input Image")
+outputs = gr.Image(label="Enhanced_Image.")
+title = "Image Super-Resolution Using ESR-GAN"
+description = "Enhance the Quality of your Low Resolution Images To High Resolution Using Artificial Intelligence"
+
+iface = gr.Interface(fn=esrgan, inputs=inputs, outputs=outputs, title=title, description=description, allow_flagging="never")
+
+iface.launch(inline = False)