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NumtaDB_Classifier_Model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b852997d2ee8b27c9adf8940c9d10bf9155997cbfedc1cfc31ddafe82c7e04e
+size 100886642

app.py

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+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = torch.hub.load('pytorch/vision:v0.10.0', 'inception_v3', pretrained=True)
+n_classes = 10
+model.fc = nn.Linear(model.fc.in_features, n_classes)
+model = model.to(device)
+
+model.load_state_dict(torch.load("NumtaDB_Classifier_Model.pth", map_location=device))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((299, 299)),
+    transforms.Grayscale(num_output_channels=3),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+label_name = ["Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Nine", "Ten"]
+
+def predict(image):
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+
+    image_tensor = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        outputs = model(image_tensor)
+        probs = torch.softmax(outputs, dim=1)
+
+    predictions = {label_name[i]: float(probs[0][i]) for i in range(len(label_name))}
+
+    return predictions
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload Image"),
+    outputs=gr.Label(num_top_classes=len(label_name)),
+    title="BanglaDigitPro: Advanced Bengali Numeral Recognition",
+    description="Upload an image of a handwritten Bangla digit to classify it.",
+    examples=[["example_1.png"], ["example_2.png"], ["example_3.png"], ["example_4.png"], ["example_5.png"]]
+)
+
+iface.launch(share=True)