Delete demo.py

demo.py

@@ -1,54 +0,0 @@
-import torch
-from models import vgg19
-import gdown
-from PIL import Image
-from torchvision import transforms
-import gradio as gr
-import cv2
-import numpy as np
-import scipy
-
-model_path = "pretrained_models/model_qnrf.pth"
-url = "https://drive.google.com/uc?id=1nnIHPaV9RGqK8JHL645zmRvkNrahD9ru"
-gdown.download(url, model_path, quiet=False)
-
-device = torch.device('cpu')  # device can be "cpu" or "gpu"
-
-model = vgg19()
-model.to(device)
-model.load_state_dict(torch.load(model_path, device))
-model.eval()
-
-
-def predict(inp):
-    inp = Image.fromarray(inp.astype('uint8'), 'RGB')
-    inp = transforms.ToTensor()(inp).unsqueeze(0)
-    inp = inp.to(device)
-    with torch.set_grad_enabled(False):
-        outputs, _ = model(inp)
-    count = torch.sum(outputs).item()
-    vis_img = outputs[0, 0].cpu().numpy()
-    # normalize density map values from 0 to 1, then map it to 0-255.
-    vis_img = (vis_img - vis_img.min()) / (vis_img.max() - vis_img.min() + 1e-5)
-    vis_img = (vis_img * 255).astype(np.uint8)
-    vis_img = cv2.applyColorMap(vis_img, cv2.COLORMAP_JET)
-    vis_img = cv2.cvtColor(vis_img, cv2.COLOR_BGR2RGB)
-    return vis_img, int(count)
-
-
-inputs = gr.Image(label="Image of Crowd")
-outputs = [
-    gr.Image(label="Predicted Density Map"),
-    gr.Label(label="Predicted Count")
-]
-
-# Assuming `title`, `desc`, and `examples` variables are defined elsewhere in your code.
-title = "Your App Title"
-desc = "Your App Description"
-
-gr.Interface(fn=predict, 
-             inputs=inputs, 
-             outputs=outputs, 
-             title=title, 
-             description=desc, 
-             allow_flagging="never").launch(share=True)