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import gradio as gr
import numpy as np
import pickle
from PIL import Image
import os
import random
# 1. Load the model
with open('model.pkl', 'rb') as f:
model_params = pickle.load(f)
W1 = model_params['W1']
b1 = model_params['b1']
W2 = model_params['W2']
b2 = model_params['b2']
# 2. Define helper functions
def ReLu(Z):
return np.maximum(Z, 0)
def softmax(Z):
return np.exp(Z) / sum(np.exp(Z))
def forward_prop(W1, b1, W2, b2, X):
Z1 = W1.dot(X) + b1
A1 = ReLu(Z1)
Z2 = W2.dot(A1) + b2
A2 = softmax(Z2)
return Z1, Z2, A1, A2
def get_predictions(A2):
return np.argmax(A2, 0)
def preprocess_image(image):
# Convert to grayscale
img = image.convert('L')
# Resize the image
img = img.resize((28, 28))
# Convert to numpy array and normalize
img_array = np.array(img).reshape(1, 28*28) / 255.0
return img_array.T # Transpose to match the shape (784, 1)
# 3. Define prediction function
def predict_digit(image):
X = preprocess_image(image)
# Forward propagation
_, _, _, A2 = forward_prop(W1, b1, W2, b2, X)
# Get the prediction
prediction = get_predictions(A2)
return int(prediction[0])
# 4. Load sample images
sample_images = []
sample_dir = "sample_images" # Make sure this directory exists in your Space
for filename in os.listdir(sample_dir):
if filename.endswith((".png", ".jpg", ".jpeg")):
img_path = os.path.join(sample_dir, filename)
sample_images.append(img_path)
# 5. Define function to select random image
def select_random_image():
return random.choice(sample_images)
# 6. Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Handwritten Digit Recognition")
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil", label="Input Image")
upload_button = gr.UploadButton("Upload Image", file_types=["image"])
sample_button = gr.Button("Use Random Sample Image")
with gr.Column():
output_label = gr.Label(label="Prediction")
predict_button = gr.Button("Predict")
upload_button.upload(fn=lambda file: file.name, inputs=upload_button, outputs=input_image)
sample_button.click(fn=select_random_image, inputs=None, outputs=input_image)
predict_button.click(fn=predict_digit, inputs=input_image, outputs=output_label)
gr.Markdown("## Sample Images")
with gr.Row():
for img_path in sample_images[:5]: # Display first 5 sample images
gr.Image(img_path, show_label=False, height=100)
with gr.Row():
for img_path in sample_images[5:10]: # Display next 5 sample images
gr.Image(img_path, show_label=False, height=100)
# 7. Launch the app
demo.launch() |