Rename app.txt to app.py

app.py

@@ -0,0 +1,79 @@
+import torch
+import torch.nn as nn
+from torchvision.transforms._transforms_video import (CenterCropVideo,NormalizeVideo)
+from torchvision.transforms import (Compose,Lambda,RandomCrop,RandomHorizontalFlip,Resize)
+from pytorchvideo.transforms import (ApplyTransformToKey,Normalize,RandomShortSideScale,UniformTemporalSubsample,Permute)
+import numpy as np
+import gradio as gr
+import spaces
+# torch.save(model.state_dict(), 'model.pth')
+video_transform = Compose([
+    ApplyTransformToKey(key = 'video',
+    transform = Compose([
+        UniformTemporalSubsample(20),
+        Lambda(lambda x:x/255),
+        Normalize((0.45,0.45,0.45),(0.225,0.225,0.225)),
+        RandomShortSideScale(min_size = 248, max_size = 256),
+        CenterCropVideo(224),
+        RandomHorizontalFlip(p=0.5),
+    ]),  
+                       ),  
+])
+#============================================================
+class mymodel_test(nn.Module):
+    def __init__(self):
+        super(mymodel_test,self).__init__()
+        self.video_model = torch.hub.load('facebookresearch/pytorchvideo','efficient_x3d_xs', pretrained=False)
+        self.relu = nn.ReLU()
+        
+        self.Linear = nn.Linear(400,1)        
+        
+    def forward(self,x):
+        
+        x = self.relu(self.video_model(x))
+        x = self.Linear(x)
+        return x
+    
+    
+#============================================================
+    
+model_test = mymodel_test()
+model_test.load_state_dict(torch.load('model.pth'))
+from pytorchvideo.data.encoded_video import EncodedVideo
+@spaces.GPU
+def interface_video(video_path):
+    video = EncodedVideo.from_path(video_path)
+    
+    video_data = video.get_clip(0,2)
+    
+    video_data = video_transform(video_data)
+    
+    video_data['video'].shape
+    model = model_test
+    
+    inputs = video_data['video']
+    
+    inputs = torch.unsqueeze(inputs, 0 )
+    inputs.shape
+    preds = model(inputs)
+    
+    preds = preds.detach().cpu().numpy()
+    preds = np.where(preds>0.5,1,0)
+    if(preds==0):
+        return 'non violence'
+    else:
+        return 'violence'
+    # return preds
+demo = gr.Blocks()
+with demo:
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                input_video = gr.Video(label='Input Video', height=360)
+                # input_video = load_video(input_video)
+            with gr.Row():
+                submit_video_button = gr.Button('Submit')
+        with gr.Column():
+                label_video = gr.Label()
+    submit_video_button.click(fn=iinterface_video, inputs=input_video, outputs=label_video)
+demo.launch()