Spaces:
Sleeping
Sleeping
File size: 1,770 Bytes
242147e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Load the model
model_path = "model_2_familyguy.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_familyguy(image):
# Preprocess image
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
image = image.resize((150, 150)) # Resize the image to 150x150
image = np.array(image) / 255.0 # Normalize the image
image = np.expand_dims(image, axis=0) # Add batch dimension
# Predict
prediction = model.predict(image)
# Convert the probabilities to rounded values
prediction = np.round(prediction, 2)
# Separate the probabilities for each class
p_brian = prediction[0][0] # Probability for class 'Brain Griffin'
p_lois = prediction[0][1] # Probability for class 'Lois Griffin'
p_peter = prediction[0][2] # Probability for class 'Peter Griffin'
p_stewie = prediction[0][3] # Probability for class 'Stewie Griffin'
return {'brian': p_brian, 'lois': p_lois, 'peter': p_peter, 'stewie': p_stewie}
# Create the Gradio interface
input_image = gr.Image()
iface = gr.Interface(
fn=predict_familyguy,
inputs=input_image,
outputs=gr.Label(num_top_classes=4), # This will display the top 4 class labels
examples=["images/Brain1.png", "images/Brain2.jpg", "images/Brain3.jpg",
"images/Lois1.png", "images/Lois2.png", "images/Lois3.png",
"images/Peter1.jpg", "images/Peter2.jpg", "images/Peter3.jpg",
"images/Stewie1.jpg", "images/Stewie2.jpg", "images/Stewie3.jpg"],
description="Upload an image to classify it as Brian, Lois, Peter, or Stewie."
)
iface.launch()
|