Create app.py

app.py

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