Upload 3 files
Browse files- app.py +62 -0
- best_model.pth +3 -0
- requirements.txt +4 -0
app.py
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import torch
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import torch.nn as nn
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from torchvision import transforms, models
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from PIL import Image
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import gradio as gr
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# ---- 1. Hiragana Classes ----
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# Replace with the exact class names from your dataset
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classes = [
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"aa", "chi", "ee", "fu", "ha", "he", "hi", "ho", "ii",
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"ka", "ke", "ki", "ko", "ku", "ma", "me", "mi", "mo", "mu",
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"na", "ne", "ni", "nn", "no", "nu", "oo",
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"ra", "re", "ri", "ro", "ru", "sa", "se", "shi", "so", "su",
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"ta", "te", "to", "tsu", "uu", "wa", "wo", "ya", "yo", "yu"
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]
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# ---- 2. Image Transform (same as training) ----
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transform = transforms.Compose([
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transforms.Lambda(lambda x: x.convert('RGB')),
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.RandomRotation(10),
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transforms.ColorJitter(),
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transforms.Normalize(mean=[0.5, 0.5, 0.5],
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std=[0.5, 0.5, 0.5])
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])
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# ---- 3. Load Model ----
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = models.resnet50(weights=None)
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in_features = model.fc.in_features
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model.fc = nn.Sequential(
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nn.Linear(in_features, 512),
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nn.ReLU(),
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nn.Dropout(0.4),
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nn.Linear(512, len(classes))
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)
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model.load_state_dict(torch.load("best_model.pth", map_location=device))
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model.to(device)
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model.eval()
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# ---- 4. Prediction Function ----
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def predict(image):
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img = transform(image).unsqueeze(0).to(device)
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with torch.no_grad():
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outputs = model(img)
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_, predicted = torch.max(outputs, 1)
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return f"Predicted: {classes[predicted.item()]}"
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# ---- 5. Gradio UI ----
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interface = gr.Interface(
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fn=predict,
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inputs=gr.Image(type="pil"),
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outputs="text",
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title="Japanese Hiragana Classifier",
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description="Upload an image of a handwritten Hiragana character and get its predicted syllable."
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)
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if __name__ == "__main__":
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interface.launch()
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best_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:05f9552870c196b41cee15f2b7b175140f36b8540c0e6fc80c4c46cba2eeabe8
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size 98642112
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requirements.txt
ADDED
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torch
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torchvision
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Pillow
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gradio
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