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---
title: Hindi Character Classifier
emoji: 🖼️
colorFrom: red
colorTo: blue
sdk: streamlit
sdk_version: "1.25.0"
library_name: torch
pipeline_tag: image-classification
---
# Hindi Character CNN
This model is a Convolutional Neural Network (CNN) for Hindi character image classification, built with PyTorch.
## Usage
This model is designed to classify images of Hindi characters. It takes a 32x32 pixel RGB image as input and outputs the predicted Hindi character class.
**To use this model within a Hugging Face Space (Streamlit example):**
1. **Ensure you have the following files in your space:**
* `your_model_file.py`: Contains the `HindiCharacterCNN` class definition.
* `your_model.safetensors`: The model's weights.
* `app.py`: The Streamlit application script.
* `requirements.txt`: Lists your dependencies (torch, torchvision, pillow, streamlit).
2. **Example `app.py` (Streamlit):**
```python
import streamlit as st
import torch
from PIL import Image
import torchvision.transforms as transforms
from your_model_file import HindiCharacterCNN # Replace with your model file
# Load model
model = HindiCharacterCNN(num_labels=36)
model.load_state_dict(torch.load("your_model.safetensors", map_location=torch.device('cpu')))
model.eval()
# Preprocessing
transform = transforms.Compose([
transforms.Resize((32, 32)),
transforms.ToTensor(),
])
st.title("Hindi Character Classification")
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = Image.open(uploaded_file).convert('RGB')
st.image(image, caption="Uploaded Image.", use_column_width=True)
st.write("")
st.write("Classifying...")
image = transform(image).unsqueeze(0)
with torch.no_grad():
output = model(image)
probabilities = torch.nn.functional.softmax(output[0], dim=0)
_, predicted_class = torch.max(probabilities, 0)
st.write(f"Predicted Class: {int(predicted_class)}") |