initial commit

app.py

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+import torch
+import gradio as gr
+import json
+from torchvision import transforms
+import torch.nn.functional as F
+
+TORCHSCRIPT_PATH = "res/screenclassification-resnet-noisystudent+web350k.torchscript"
+LABELS_PATH = "res/class_map_enrico.json"
+IMG_SIZE = 128
+
+model = torch.jit.load(TORCHSCRIPT_PATH)
+
+with open(LABELS_PATH, "r") as f:
+	label2Idx = json.load(f)["label2Idx"]
+	
+img_transforms = transforms.Compose([
+	transforms.Resize(IMG_SIZE),
+	transforms.ToTensor(),
+	transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+	
+def predict(img):
+	img_input = img_transforms(img).unsqueeze(0)
+	predictions = F.softmax(model(img_input), dim=-1)[0]
+	confidences = {}
+	for label in label2Idx:
+		confidences[label] = float(predictions[int(label2Idx[label])])
+		
+	return confidences
+	
+example_imgs = [
+    "examples/example.jpg",
+	"examples/example_pair1.jpg",
+	"examples/example_pair2.jpg"
+]
+
+interface = gr.Interface(fn=predict, inputs=gr.Image(type="pil"), outputs=gr.Label(num_top_classes=3), examples=example_imgs)
+
+interface.launch()

res/class_map_enrico.json

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+{"label2Idx": {"bare": 0, "calculator": 1, "camera": 2, "chat": 3, "editor": 4, "form": 5, "gallery": 6, "list": 7, "login": 8, "maps": 9, "mediaplayer": 10, "menu": 11, "modal": 12, "news": 13, "other": 14, "profile": 15, "search": 16, "settings": 17, "terms": 18, "tutorial": 19}, "idx2Label": {"0": "bare", "1": "calculator", "2": "camera", "3": "chat", "4": "editor", "5": "form", "6": "gallery", "7": "list", "8": "login", "9": "maps", "10": "mediaplayer", "11": "menu", "12": "modal", "13": "news", "14": "other", "15": "profile", "16": "search", "17": "settings", "18": "terms", "19": "tutorial"}}