Create app.py

app.py

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+import torch
+import gradio as gr
+from torchvision import transforms as T
+from torch.utils.data import DataLoader
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+import imageio
+
+
+classes=["Other","Anger","Contempt","Happiness","Surprise"]
+
+
+# load a resnet18 model pretrained on ImageNet
+# and turn off autograd on model's parameters
+def load_model(idx):
+  model = torch.jit.load('/content/model_2_60acc.pt',map_location=torch.device('cpu')).eval()
+  for param in model.parameters():
+    param.requires_grad = False
+  return model
+model=load_model(0)
+
+# preprocess data
+pretrained_std = torch.Tensor([0.229, 0.224, 0.225])
+pretrained_mean = torch.Tensor([0.485, 0.456, 0.406])
+
+optical_flow_t = T.Compose([
+                       T.Resize((224,224)),
+                       T.ToTensor(),
+                       T.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225]),
+                       ])  
+
+
+
+
+    
+ogpic=['/content/ogtest0.png','/content/ogtest1.png','/content/ogtest2.png','/content/ogtest3.png','/content/ogtest4.png','/content/ogtest5.png']
+ofpic=['/content/oftest0.jpg','/content/oftest1.jpg','/content/oftest2.jpg','/content/oftest3.jpg','/content/oftest4.jpg','/content/oftest5.jpg']
+diffpic=['/content/difftest0.png','/content/difftest1.png','/content/difftest2.png','/content/difftest3.png','/content/difftest4.png','/content/difftest5.png']
+exp=[['/content/ogtest0.png'],['/content/ogtest1.png'],['/content/ogtest2.png'],['/content/ogtest3.png'],['/content/ogtest4.png'],['/content/ogtest5.png']]
+vid=["/content/vidtest0.mp4","/content/vidtest1.mp4","/content/vidtest2.mp4","/content/vidtest3.mp4","/content/vidtest4.mp4","/content/vidtest5.mp4"]
+actual=["Contempt","Other","Happiness","Anger","Other","Contempt"]
+def main():
+
+    with gr.Blocks() as demo:
+        aa=gr.Variable(value=0)
+        def set_example_image(img):
+          aa.value=img
+          return gr.Image.update(value=exp[img][0])
+
+        def predss(img):
+          #print(Image.open(ofpic[a]).shape())
+          processed_img = optical_flow_t(Image.open(ofpic[aa.value]))
+          tb = torch.unsqueeze(processed_img, dim=0)
+          loaded_test = DataLoader(tb, batch_size=1,shuffle=False)
+          # get predictions
+          for i, inputs in enumerate(loaded_test):
+            with torch.no_grad():
+                output = model(inputs.to(torch.device('cpu'))) # Feed Network
+                probs = torch.nn.functional.softmax(output[0], dim=0)
+            top5_prob, top5_idx = torch.topk(probs, 5)
+            preds = {classes[idx]: prob.item() for idx, prob in zip(top5_idx, top5_prob)}
+            return ogpic[aa.value],ofpic[aa.value],diffpic[aa.value],vid[aa.value], preds,actual[aa.value]
+        gr.Markdown('''## Micro-expression recognition
+                    
+                    ''')
+                                                 
+        with gr.Box():
+            input_image = gr.Image(type="pil", label="Input Image")
+            example_images = gr.Dataset(components=[input_image],
+                                        samples=[['/content/ogtest0.png'],['/content/ogtest1.png'],['/content/ogtest2.png'],['/content/ogtest3.png'],['/content/ogtest4.png'],['/content/ogtest5.png']]
+                                        ,type="index")
+
+            with gr.Row():
+                btn = gr.Button("Process")
+        gr.Markdown('''### Original Image''')
+        with gr.Box():
+            with gr.Row():
+                img_before = gr.Image(label="Original Image")
+                img_after1 = gr.Image(label="Different frame")
+            with gr.Row():
+                img_after = gr.Image(label="Optical flow") 
+                label_predict = gr.Label(label="Prediction")
+        with gr.Box():
+            with gr.Row():
+                video = gr.Video(label="Original Video")
+            with gr.Row():
+                label_actual=gr.Label(label="Actual Emotion")
+
+        
+        # events
+        btn.click(fn=predss, 
+                  inputs=[input_image], 
+                  outputs=[img_before,img_after,img_after1,video,label_predict,label_actual])
+        
+        example_images.click(fn=set_example_image,
+                                 inputs=example_images,
+                                 outputs=example_images.components)
+
+    demo.launch()
+
+if __name__ == '__main__':
+    main()