files uploaded

app.py

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+import math
+import io
+import os
+import torch
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from pytorch_msssim import ms_ssim
+
+# loading the models
+from compressai.zoo import bmshj2018_factorized 
+from compressai.zoo import bmshj2018_hyperprior 
+from compressai.zoo import cheng2020_anchor
+
+import gradio as gr
+
+# function to compress the image 
+def image_compress(input_img):
+    # removing the file from folder
+
+    folder_path = "./result/"
+    file_name = "compressed.jpg"
+
+    file_path = os.path.join(folder_path, file_name)
+    # checking the compressed file exist or not
+    if file_path:
+        try:
+            os.remove(file_path)
+            print(f"File {file_name} deleted successfully.")
+        except OSError as e:
+            # print(f"Error: {file_path} - {e.strerror}")
+            pass
+   
+    # checking the device
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+    net = bmshj2018_factorized(quality=1, pretrained=True).eval().to(device)
+
+    print(f'Parameters: {sum(p.numel() for p in net.parameters())}')
+
+    # img = Image.open(input_img).convert('RGB')
+    img = input_img.convert('RGB') 
+    x = transforms.ToTensor()(img).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        out_net = net.forward(x)
+    out_net['x_hat'].clamp_(0, 1)
+
+    rec_net = transforms.ToPILImage()(out_net['x_hat'].squeeze().cpu())
+
+    rec_net.save("./result/compressed.jpg")
+
+    output_image = "./result/compressed.jpg"
+
+    # print("Your input image path is:::")
+    # print(input_img.name)
+    
+    # Split the file path into components
+    # components = input_img.split('/')
+
+    # Get the last component (file name and extension)
+    # file_name_with_extension = components[-1]
+
+    # Split the file name and extension using rsplit()
+    # file_name, file_extension = file_name_with_extension.rsplit('.', 1)
+
+    # rec_net.save("./result/"+file_name+".jpg")
+
+    # output_image = "./result/" + file_name + ".jpg"
+
+    # calculatinig the reduction size
+    # file_size_bytes1 = os.path.getsize(input_img)
+    # file_size_bytes2 = os.path.getsize(output_image)
+    # file_size_mb1 = file_size_bytes1 / 1000000
+    # file_size_mb2 = file_size_bytes2 / 1000000
+    # final_percent = ((file_size_mb1 - file_size_mb2) / file_size_mb1) * 100
+
+    return output_image, output_image
+
+# defining the components the inference
+input_component = gr.Image(type="pil")
+output_component = [gr.Image(type="pil"), gr.File(label="Download", extension=".png")]
+
+interface = gr.Interface(
+    fn=image_compress,
+    inputs=input_component,
+    outputs=output_component
+)
+
+interface.launch()