Upload app.py

app.py

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+import torch
+#import streamlit as st
+from torchvision import transforms
+import torch
+import json
+from torch import nn
+from torchvision import models
+from PIL import Image
+from torch.nn import functional as F
+
+# load idx_to_class.json into dictionary
+with open('class_to_idx.json', 'r') as f:
+    class_to_idx = json.load(f)
+    
+idx_to_class = {v: k for k, v in class_to_idx.items()}
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# model
+model = models.resnet18(pretrained=True)
+num_ftrs = model.fc.in_features
+model.fc = nn.Linear(num_ftrs, len(idx_to_class))
+model.load_state_dict(torch.load('gardenbirds.pth', map_location=torch.device('cpu')))
+model.eval()
+
+data_transforms = {
+    'val': transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ]),
+}
+
+def predict(inp):
+    # transform pil image to tensor
+    inp = data_transforms['val'](inp).unsqueeze(0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+        #print(prediction)
+        confidences = {idx_to_class[i]: float(prediction[i]) for i in range(26)}
+    return confidences 
+
+interface = gr.Interface(fn=predict, 
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=3),
+             title="What garden bird is this?",
+             examples=["gardenbirds/Parus major/345c506a-6ed5-4f9c-a7d3-4d0cb4af88fc.jpg"])
+
+interface.launch(share=True)