Added app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+import time
+from PIL import Image
+import torch
+from torch import nn
+import timm
+from custom_torch_module import setup_utils
+
+
+
+title = "Age Prediction model"
+description = "ViT(medium clip) based model. transfer trained with custom dataset"
+article = "Through bunch of fine tuning and experiments. REMEMBER! This model can be wrong."
+
+MODEL_NAME = "vit_medium_patch16_clip_224.tinyclip_yfcc15m"
+FILE_NAME = "pretrained_weight/vit_medium_patch16_clip_224.tinyclip_yfcc15m(trainable 0.00) (eval Score 0.9067, Loss 29.465482).pth"
+DEVICE = "cpu"
+
+
+
+
+torch.set_default_device(DEVICE)
+
+model = timm.create_model(MODEL_NAME, pretrained=True, num_classes=0, drop_rate=0.7)
+model_classifier = nn.Sequential(nn.Linear(512, 512),
+                                nn.BatchNorm1d(512),
+                                nn.GELU(),
+                                nn.Linear(512, 1))
+model = nn.Sequential(model, model_classifier)
+
+test_transform = setup_utils.build_transform(img_size=224, is_data_aug=False)
+model.load_state_dict(state_dict=torch.load(FILE_NAME, weights_only=True))
+
+
+
+
+def predict(img):
+    start_time = time.time()
+    model.eval()
+    with torch.inference_mode():
+        img = test_transform(img).unsqueeze(dim=0).to(DEVICE)
+        pred_age = model(img).item()
+    
+    end_time = time.time()
+    
+    elapsed_time = end_time - start_time
+    fps = 1 / elapsed_time
+    return pred_age, fps
+
+
+
+demo = gr.Interface(fn=predict, 
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Number(label="Age Prediction"),
+                             gr.Number(label="Prediction speed (fps)")], 
+                    title=title,
+                    description=description,
+                    article=article)
+demo.launch()