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import numpy as np
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
from PIL import Image
(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()
# Scaling array values so we get values form 0 to 1
X_train = X_train / 255
X_test = X_test / 255
# Define a simple feedforward neural network
model2 = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)), # Flatten the 28x28 images
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(10, activation='softmax')
])
# Compile the model
model2.compile(optimizer='adam',
loss='sparse_categorical_crossentropy', # Corrected the loss function
metrics=['accuracy'])
# Train the model
model2.fit(X_train, y_train, epochs=5) # Assuming X_train and y_train are properly loaded
# Function to preprocess the uploaded image
def preprocess_image(input_image_path): # Accept file path as input
# Load the image using PIL
image = Image.open(input_image_path)
# Resize and convert the image to grayscale
image = image.resize((28, 28)).convert('L')
# Convert the image to a NumPy array
image_array = np.array(image)
# Normalize the pixel values
image_array = image_array / 255.0
return image_array
# Function to make predictions
def predict_digit(input_image_path):
# Preprocess the image
image_array = preprocess_image(input_image_path)
# Reshape the image_array
image_array = image_array.reshape(1, 28, 28)
prediction = model2.predict(image_array)
predicted_digit = np.argmax(prediction)
otpt = f"Predicted digit: {predicted_digit}"
return str(otpt)
import gradio as gr
iface = gr.Interface(
fn=predict_digit,
inputs=gr.Image(type="filepath", label="Upload Image"),
outputs=gr.Textbox("Predicted Digit"),
)
iface.launch(share=True)
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