Create app.py

app.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+from torchvision import transforms, datasets, models
+import gradio as gr
+
+transformer = models.ResNet18_Weights.IMAGENET1K_V1.transforms()
+transformer
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class_names = ['Ahegao', 'Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
+classes_count = len(class_names)
+
+model = models.resnet18(weights='DEFAULT').to(device)
+model.fc = nn.Sequential(
+  nn.Linear(512, classes_count)
+)
+model.load_state_dict(torch.load('./model_params.pt', map_location=device), strict=False)
+
+def predict(image):
+  transformed_image = transformer(image).unsqueeze(0).to(device)
+  model.eval()
+
+  with torch.inference_mode():
+    pred = torch.softmax(model(transformed_image), dim=1)
+
+  pred_and_labels = {class_names[i]: pred[0][i].item() for i in range(len(pred[0]))}
+
+  return pred_and_labels
+
+title = "Emotion Checker"
+description = "Can classify 6 emotions: Ahegao, Angry, Happy, Neutral, Sad, Surprise"
+
+examples = [
+    './example_1.jpg',
+    './example_2.jpg',
+    './example_3.jpg',
+    './example_4.jpg',
+    './example_5.jpg',
+    './example_6.jpg',
+]
+
+
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes=classes_count, label="Predictions")],
+    examples=examples,
+    title=title,
+    description=description
+)
+
+app.launch(
+    share=True, 
+    height=800
+)