Delete [skooldio]_thai_handwritten_recognition_app_by_gradio (2).py

[skooldio]_thai_handwritten_recognition_app_by_gradio (2).py

@@ -1,102 +0,0 @@
-# -*- coding: utf-8 -*-
-"""[Skooldio] Thai Handwritten Recognition App by Gradio
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1Feza00drEEejwPVzgdTmz60KArcms_w_
-"""
-
-!pip install gradio==3.35.0
-!pip install torchvision
-
-!wget https://github.com/biodatlab/deep-learning-skooldio/raw/master/saved_model/thai_digit_net.pth
-
-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):
-    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)
-
-    with torch.no_grad():
-        probs = model(img_tensor).softmax(dim=1).squeeze()
-
-    probs, indices = torch.topk(probs, 5)
-    return {LABELS[i]: float(p) for i, p in zip(indices.tolist(), probs.tolist())}
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Sketchpad(
-        label="Draw Here",
-        image_mode="L",
-        width=400,
-        height=350
-    ),
-    outputs=gr.Label(label="Guess"),
-    title="Thai Digit Handwritten Classification",
-    description="วาดเลขไทยตั้งแต่ ๐ ถึง ๙",
-    live=True
-)
-
-if __name__ == "__main__":
-    demo.launch()
-
-gr.Interface(
-    fn=predict,
-    inputs=gr.Sketchpad(label="Draw Here", brush_radius=5, type="pil", shape=(120, 120)),
-    outputs=gr.Label(label="Guess"),
-    title="Thai Digit Handwritten Classification",
-    live=True
-).launch(enable_queue=True)
-