Update app.py

app.py

@@ -1,140 +1,6 @@
-import os
-import cv2
-import numpy as np
-import torch
-from torchvision import transforms
-from PIL import Image
-import gradio as gr
-
-# Set up device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# Clone model repository if needed
-if not os.path.exists("DIS"):
-    os.system("git clone https://github.com/xuebinqin/DIS")
-    os.system("mv DIS/IS-Net/* .")
-
-# Import model components
-from models import ISNetDIS
-from data_loader_cache import normalize
-
-# Create model directory
-os.makedirs("saved_models", exist_ok=True)
-if os.path.exists("isnet.pth"):
-    os.rename("isnet.pth", "saved_models/isnet.pth")
-
-# Define image preprocessing
-class ImageNormalizer:
-    def __init__(self, mean=[0.5, 0.5, 0.5], std=[1.0, 1.0, 1.0]):
-        self.mean = mean
-        self.std = std
-    
-    def __call__(self, img):
-        return normalize(img, self.mean, self.std)
-
-transform = transforms.Compose([ImageNormalizer()])
-
-# Load and configure model
-model_config = {
-    "model_path": "saved_models",
-    "model_file": "isnet.pth",
-    "input_size": [1024, 1024],
-    "device": device
-}
-
-model = ISNetDIS().to(device)
-if os.path.exists(f"{model_config['model_path']}/{model_config['model_file']}"):
-    model.load_state_dict(
-        torch.load(
-            f"{model_config['model_path']}/{model_config['model_file']}",
-            map_location=device
-        )
-    )
-model.eval()
-
-def process_image(input_image):
-    """Process an image through the segmentation model"""
-    try:
-        # Convert Gradio input to usable image path
-        if hasattr(input_image, 'name'):
-            image_path = input_image.name
-        else:
-            image_path = input_image
-        
-        # Read and preprocess image
-        img = cv2.imread(image_path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = torch.from_numpy(img).float().permute(2, 0, 1) / 255.0
-        img = transform(img).unsqueeze(0).to(device)
-        
-        # Get prediction
-        with torch.no_grad():
-            pred = model(img)[0][0]
-            pred = torch.sigmoid(pred[0])
-            pred = (pred - pred.min()) / (pred.max() - pred.min() + 1e-8)
-            mask = (pred.cpu().numpy() * 255).astype(np.uint8)
-        
-        # Create output images
-        original_img = Image.open(image_path).convert("RGB")
-        mask_img = Image.fromarray(mask).convert("L")
-        transparent_img = original_img.copy()
-        transparent_img.putalpha(mask_img)
-        
-        return transparent_img, mask_img
-    
-    except Exception as e:
-        raise gr.Error(f"Error processing image: {str(e)}")
-
-# Gradio interface setup
-title = "Image Background Removal"
-description = """
-Upload an image to automatically remove the background using DIS (Dichotomous Image Segmentation).
-<br>Model from: <a href="https://github.com/xuebinqin/DIS">xuebinqin/DIS</a>
-"""
-
-# Check for example images
-examples = []
-for img_file in ["robot.png", "ship.png"]:
-    if os.path.exists(img_file):
-        examples.append([img_file])
-
-# Create interface
-with gr.Blocks() as app:
-    gr.Markdown(f"## {title}")
-    gr.Markdown(description)
-    
-    with gr.Row():
-        input_col = gr.Column()
-        output_col = gr.Column()
-    
-    with input_col:
-        image_input = gr.Image(type="filepath", label="Upload Image")
-        submit_btn = gr.Button("Remove Background", variant="primary")
-    
-    with output_col:
-        transparent_output = gr.Image(label="Transparent Result", type="pil")
-        mask_output = gr.Image(label="Segmentation Mask", type="pil")
-    
-    if examples:
-        gr.Examples(
-            examples=examples,
-            inputs=image_input,
-            outputs=[transparent_output, mask_output],
-            fn=process_image,
-            cache_examples=True,
-            label="Try Example Images"
-        )
-    
-    submit_btn.click(
-        fn=process_image,
-        inputs=image_input,
-        outputs=[transparent_output, mask_output]
-    )
-
-# Launch the app
-if __name__ == "__main__":
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )
+torch>=1.10.0
+torchvision>=0.11.0
+opencv-python>=4.5.0
+numpy>=1.21.0
+Pillow>=9.0.0
+gradio>=3.0