Update app.py

app.py

@@ -482,16 +482,19 @@ def generate_example():
         # get random input
         data_path = 'EchoNet-Dynamic'
         image_data = Echo(root = data_path, split = 'all', target_type=['Filename','LargeIndex','SmallIndex'])
-        image_loaded_data = DataLoader(image_data, batch_size=1, shuffle=True)
+        image_loaded_data = DataLoader(image_data, batch_size=30, shuffle=True)
         val_data = next(iter(image_loaded_data))
         #create_echo_clip(val_data,'test')
         val_seq = val_data[0]
-        filename = val_data[1][0][0]
-        video = f"EchoNet-Dynamic/Videos/{filename}"
+    
         val_tensor = torch.tensor(val_seq, dtype=torch.float32) 
-        results = model(val_tensor)
+        n=random.randint(0, 29)
+        results = model(val_tensor)[n]
+
+        filename = val_data[1][0][n]
+        video = f"EchoNet-Dynamic/Videos/{filename}"
 
-        plot, Rm, Ra, Emax, Emin, Vd,Tc, start_v = pvloop_simulator(Rm=round(results[0][4].item(),2), Ra=round(results[0][5].item(),2), Emax=results[0][2].item(), Emin=round(results[0][3].item(),2), Vd=round(results[0][6].item(),2), Tc=round(results[0][0].item(),2), start_v=round(results[0][1].item(),2))
+        plot, Rm, Ra, Emax, Emin, Vd,Tc, start_v = pvloop_simulator(Rm=round(results[4].item(),2), Ra=round(results[5].item(),2), Emax=results[2].item(), Emin=round(results[3].item(),2), Vd=round(results[6].item(),2), Tc=round(results[0].item(),2), start_v=round(results[1].item(),2))
         video = video.replace("avi", "mp4")
         return video, plot, Rm, Ra, Emax, Emin, Vd, Tc, start_v