Upload app.py

app.py

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+import numpy as np
+import torch
+from pathlib import Path
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from torchvision import transforms
+import gradio as gr
+
+
+transform = transforms.Compose([
+    transforms.Resize((28, 28)),
+    transforms.Grayscale(),
+    transforms.ToTensor()
+])
+labels = ["๐ (ศูนย์)", "๑ (หนึ่ง)", "๒ (สอง)", "๓ (สาม)", "๔ (สี่)", "๕ (ห้า)", "๖ (หก)", "๗ (เจ็ด)", "๘ (แปด)", "๙ (เก้า)"]
+LABELS = {i:k for i, k in enumerate(labels)} # dictionary of index and label
+
+
+# Load model using DropoutThaiDigit instead
+class DropoutThaiDigit(nn.Module):
+    def __init__(self):
+        super(DropoutThaiDigit, self).__init__()
+        self.fc1 = nn.Linear(28 * 28, 392)
+        self.fc2 = nn.Linear(392, 196)
+        self.fc3 = nn.Linear(196, 98)
+        self.fc4 = nn.Linear(98, 10)
+        self.dropout = nn.Dropout(0.1)
+        
+    def forward(self, x):
+        x = x.view(-1, 28 * 28)
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = self.dropout(x)
+        x = self.fc2(x)
+        x = F.relu(x)
+        x = self.dropout(x)
+        x = self.fc3(x)
+        x = F.relu(x)
+        x = self.dropout(x)
+        x = self.fc4(x)
+        return x
+
+
+model = DropoutThaiDigit()
+model.load_state_dict(torch.load("thai_digit_net.pth"))
+model.eval()
+
+
+def predict(img):
+    """
+    Predict function takes image and return top 5 predictions
+    as a dictionary:
+        {label: confidence, label: confidence, ...}
+    """
+    if img.get("composite") is not None:
+        if img["composite"].sum() == 0:
+            return {"No input sketch": 0.0}
+
+    img_data = img['composite']
+    img_gray = Image.fromarray(img_data).convert('L').resize((28, 28))
+    img_tensor = transforms.ToTensor()(img_gray).unsqueeze(0)
+
+    # Make prediction
+    with torch.no_grad():
+        probs = model(img_tensor).softmax(dim=1).squeeze()
+
+    probs, indices = torch.topk(probs, 5)  # select top 5
+    probs, indices = probs.tolist(), indices.tolist()  # transform to list
+    return {LABELS[i]: float(v) for i, v in zip(indices, probs)}
+
+js_func = """
+function refresh() {
+    const url = new URL(window.location);
+
+    if (url.searchParams.get('__theme') !== 'dark') {
+        url.searchParams.set('__theme', 'dark');
+        window.location.href = url.href;
+    }
+}
+"""
+
+with gr.Blocks(js=js_func) as demo:
+    gr.Interface(
+        fn=predict,
+        inputs=gr.Sketchpad(
+            label="Draw Here",
+            brush=gr.Brush(default_size=14, default_color="#FFFFFF", colors=["#FFFFFF"]),
+            image_mode="L",
+            layers=False,
+            eraser=None,
+            width=400,
+            height=350
+        ),
+        outputs=gr.Label(label="Guess"),
+        title="Thai Digit Handwritten Classification",
+        description="ทดลองวาดภาพตัวอักษรเลขไทยลงใน Sketchpad ด้านล่างเพื่อทำนายผลตัวเลข ตั้งแต่ ๐ (ศูนย์) ๑ (หนึ่ง) ๒ (สอง) ๓ (สาม) ๔ (สี่) ๕ (ห้า) ๖ (หก) ๗ (เจ็ด) ๘ (แปด) จนถึง ๙ (เก้า)",
+        live=True
+    )
+
+if __name__ == "__main__":
+    demo.launch()