Update app.py

app.py

@@ -1,20 +1,19 @@
 import os
-os.system("pip install gradio==2.9b23")
 import random
+os.system("pip install gradio==2.9b23")
 import gradio as gr
 from PIL import Image
 import torch
-from random import randint
-import sys
 from subprocess import call
-import psutil
-
-
 
+# Install necessary packages
+os.system("pip install gradio==2.9b23")
+os.system("pip install basicsr")
+os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
+os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")
 
 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)
@@ -22,47 +21,121 @@ def run_cmd(command):
     except KeyboardInterrupt:
         print("Process interrupted")
         sys.exit(1)
-run_cmd("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
-run_cmd("pip install basicsr")
-run_cmd("pip freeze")
 
+def inference(img, mode):
+    _id = random.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.LANCZOS)
+    img.save(INPUT_DIR + "1.jpg", "JPEG")
+    
+    if mode == "base":
+        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i " + INPUT_DIR + " -o " + OUTPUT_DIR)
+    else:
+        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i " + INPUT_DIR + " -o " + OUTPUT_DIR)
+    
+    return os.path.join(OUTPUT_DIR, "1_out.jpg")
+
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Real-ESRGAN")
+        gr.Markdown(
+            "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."
+            "\n\n"
+            "<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>"
+        )
+        
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(type="pil", label="Input")
+                model_type = gr.Radio(["base", "anime"], type="value", default="base", label="Model type")
+                examples = gr.Examples(examples=[['bear.jpg', 'base'], ['anime.png', 'anime']], inputs=[input_image, model_type])
+                submit_btn = gr.Button("Submit")
+            
+            with gr.Column():
+                output_image = gr.Image(type="file", label="Output")
+
+        submit_btn.click(fn=inference, inputs=[input_image, model_type], outputs=output_image)
+    
+    demo.launch()
+
+if __name__ == "__main__":
+    main()
+ import os
+import random
+import gradio as gr
+from PIL import Image
+import torch
+from subprocess import call
+
+# Install necessary packages
+
+os.system("pip install basicsr")
+os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P .")
 os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth -P .")
 
+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 inference(img,mode):
-    _id = randint(1, 10000)
+def run_cmd(command):
+    try:
+        print(command)
+        call(command, shell=True)
+    except KeyboardInterrupt:
+        print("Process interrupted")
+        sys.exit(1)
+
+def inference(img, mode):
+    _id = random.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.LANCZOS)
+    wpercent = (basewidth / float(img.size[0]))
+    hsize = int((float(img.size[1]) * float(wpercent)))
+    img = img.resize((basewidth, hsize), Image.LANCZOS)
     img.save(INPUT_DIR + "1.jpg", "JPEG")
+    
     if mode == "base":
-        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
+        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus -i " + INPUT_DIR + " -o " + OUTPUT_DIR)
     else:
-        os.system("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i "+ INPUT_DIR + " -o " + OUTPUT_DIR)
+        run_cmd("python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i " + INPUT_DIR + " -o " + OUTPUT_DIR)
+    
     return os.path.join(OUTPUT_DIR, "1_out.jpg")
 
-
-
+def main():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Real-ESRGAN")
+        gr.Markdown(
+            "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."
+            "\n\n"
+            "<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>"
+        )
         
-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>"
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(type="pil", label="Input")
+                model_type = gr.Radio(["base", "anime"], type="value", default="base", label="Model type")
+                examples = gr.Examples(examples=[['bear.jpg', 'base'], ['anime.png', 'anime']], inputs=[input_image, model_type])
+                submit_btn = gr.Button("Submit")
+            
+            with gr.Column():
+                output_image = gr.Image(type="file", label="Output")
+
+        submit_btn.click(fn=inference, inputs=[input_image, model_type], outputs=output_image)
+    
+    demo.launch()
 
-gr.Interface(
-    inference, 
-    [gr.inputs.Image(type="pil", label="Input"),gr.inputs.Radio(["base","anime"], type="value", default="base", label="model type")], 
-    gr.outputs.Image(type="file", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    examples=[
-    ['bear.jpg','base'],
-    ['anime.png','anime']
-    ]).launch()
+if __name__ == "__main__":
+    main()