Create app.py

app.py

@@ -0,0 +1,57 @@
+import os
+os.system('pip install gradio --upgrade')
+os.system('pip freeze')
+import random
+import gradio as gr
+from PIL import Image
+import torch
+from random import randint
+import sys
+from subprocess import call
+import psutil
+torch.hub.download_url_to_file('http://people.csail.mit.edu/billf/project%20pages/sresCode/Markov%20Random%20Fields%20for%20Super-Resolution_files/100075_lowres.jpg', 'bear.jpg')
+  
+    
+def run_cmd(command):
+    try:
+        print(command)
+        call(command, shell=True)
+    except KeyboardInterrupt:
+        print("Process interrupted")
+        sys.exit(1)
+run_cmd("wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P .")
+
+run_cmd("pip install basicsr")
+run_cmd("pip freeze")
+#run_cmd("python setup.py develop")
+def inference(img):
+    _id = randint(1, 10000)
+    INPUT_DIR = "/tmp/input_image" + str(_id) + "/"
+    OUTPUT_DIR = "/tmp/output_image" + str(_id) + "/"
+    run_cmd("rm -rf " + INPUT_DIR)
+    run_cmd("rm -rf " + OUTPUT_DIR)
+    run_cmd("mkdir " + INPUT_DIR)
+    run_cmd("mkdir " + OUTPUT_DIR)
+    basewidth = 256
+    wpercent = (basewidth/float(img.size[0]))
+    hsize = int((float(img.size[1])*float(wpercent)))
+    img = img.resize((basewidth,hsize), Image.ANTIALIAS)
+    img.save(INPUT_DIR + "1.jpg", "JPEG")
+    run_cmd("python inference_gfpgan.py --upscale 2 --test_path "+ INPUT_DIR + " --save_root " + OUTPUT_DIR)
+    return os.path.join(OUTPUT_DIR, "1_out.jpg")
+        
+title = "Real-ESRGAN"
+description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>"
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="pil", label="Input")], 
+    gr.outputs.Image(type="file", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+    ['bear.jpg']
+    ],
+    enable_queue=True
+    ).launch(debug=True)