added gradio_demo.py

gradio_demo.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import torch
+import requests
+from torchvision import transforms
+
+def predict(mfile,patch_file,positions_file,expName):
+    positions = torch.from_numpy(np.load(positions_file))
+
+    patch_h5   = h5py.File(patch_file,'r')
+    n_frames   = len(patch_h5)
+    
+    transform_norm = transforms.ToTensor()
+    patches  = torch.zeros(size=(len(positions),15,15))
+    frame_nr = np.zeros(shape=(len(positions),))
+    patch_nr = np.zeros(shape=(len(positions),))
+    
+    j=0
+    for i in range(1,n_frames+1):
+        k=0
+        for patch in patch_h5['frame_nr%s' %i]:
+            patches[j]  = transform_norm(np.array(patch,dtype=np.uint8))
+            frame_nr[j] = i 
+            patch_nr[j] = k
+            j+=1
+            k+=1 
+
+    transformed_patches = patches
+    transformed_patches = torch.reshape(transformed_patches,(len(patches),1,15,15))
+
+    inp = transforms.ToTensor()(inp).unsqueeze(0)
+    model=torch.load(m_file)
+    with torch.no_grad():
+        for i, (inputs, labels) in enumerate(dl_valid):
+            inputs = inputs.to(device)
+            y_pred = model(inputs)
+            y_pred = y_pred.cpu().numpy()
+            labels = labels.cpu().numpy()
+            plot_error(y_pred,labels,args.expName)
+            mse    += np.power(y_pred[:,0] - labels[:,0],2) + np.power(y_pred[:,1] - labels[:,1],2)
+             
+interface=gr.Interface(fn=predict, inputs={"m_file": "upload", "patch_file": "upload", "positions_file":"upload", "expName": "text"}, outputs={"output":"text"})
+interface.launch()