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(text="Predicted Digit"), ) iface.launch()