Upload app.py

app.py

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+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)