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README.md

@@ -1,3 +1,49 @@
+---
+title: Digit Recognition with CNN
+emoji: 🔢
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.19.2
+app_file: app.py
+pinned: false
+---
+
+# Digit Recognition Model
+
+This model is trained to recognize handwritten digits from the MNIST dataset.
+
+## Model Description
+
+- **Model Type:** CNN with Attention
+- **Task:** Image Classification
+- **Input:** 28x28 grayscale images
+- **Output:** Digit classification (0-9)
+
+## Training
+
+The model was trained on the MNIST dataset using a CNN architecture with attention mechanisms.
+
+## Usage
+
+```python
+import tensorflow as tf
+import numpy as np
+
+# Load the model
+model = tf.saved_model.load('https://huggingface.co/nivashuggingface/digit-recognition/resolve/main/saved_model')
+
+# Prepare input
+image = tf.keras.preprocessing.image.load_img("digit.png", target_size=(28, 28))
+image = tf.keras.preprocessing.image.img_to_array(image)
+image = image.astype('float32') / 255.0
+image = np.expand_dims(image, axis=0)
+
+# Make prediction
+predictions = model(image)
+predicted_digit = tf.argmax(predictions, axis=1).numpy()[0]
+```
+
 # AI Model Training Project
 
 This project demonstrates a complete machine learning workflow from data preparation to model deployment, using the MNIST dataset with an innovative approach to digit recognition.