app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import io

# Load the model from Hugging Face
MODEL_PATH = "https://huggingface.co/nivashuggingface/digit-recognition/resolve/main/saved_model"
model = tf.saved_model.load(MODEL_PATH)

def preprocess_image(img):
    """Preprocess the drawn image for prediction"""
    # Convert to grayscale and resize
    img = img.convert('L')
    img = img.resize((28, 28))
    # Convert to numpy array and normalize
    img_array = np.array(img)
    img_array = img_array.astype('float32') / 255.0
    # Add batch dimension
    img_array = np.expand_dims(img_array, axis=0)
    # Add channel dimension
    img_array = np.expand_dims(img_array, axis=-1)
    return img_array

def predict_digit(img):
    """Predict digit from drawn image"""
    try:
        # Preprocess the image
        processed_img = preprocess_image(img)
        # Make prediction
        predictions = model(processed_img)
        predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]
        # Get confidence scores
        confidence_scores = tf.nn.softmax(predictions[0]).numpy()
        # Create result string
        result = f"Predicted Digit: {predicted_digit}\n\nConfidence Scores:\n"
        for i, score in enumerate(confidence_scores):
            result += f"Digit {i}: {score:.2%}\n"
        return result
    except Exception as e:
        return f"Error during prediction: {str(e)}"

# Create Gradio interface
iface = gr.Interface(
    fn=predict_digit,
    inputs=gr.Image(type="pil", label="Draw a digit (0-9)"),
    outputs=gr.Textbox(label="Prediction Results"),
    title="Digit Recognition with CNN",
    description="""
    Draw a digit (0-9) in the box below. The model will predict which digit you drew.
    
    Instructions:
    1. Click and drag to draw a digit
    2. Make sure the digit is clear and centered
    3. The model will show the predicted digit and confidence scores
    """,
    examples=[
        ["examples/0.png"],
        ["examples/1.png"],
        ["examples/2.png"],
    ],
    theme=gr.themes.Soft(),
    allow_flagging="never"
)

# Launch the interface
if __name__ == "__main__":
    iface.launch()