Spaces:
Sleeping
Sleeping
File size: 1,517 Bytes
d22c3e6 f57229d d22c3e6 7200bfe |
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 47 48 49 50 |
import gradio as gr
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
import numpy as np
# Load the model with weights
model_path = "my_model.h5" # Replace with the correct path to your model
model = load_model(model_path)
# Define the image height and width
image_height = 224
image_width = 224 # Adjust to match the input size of your model
# Map the class indices to class names based on your dataset
class_names = {
0: "Class 0",
1: "Class 1",
2: "Class 2",
3: "Class 3",
4: "Class 4",
}
# Define the prediction function
def predict_image(img_array):
# Preprocess the input image
img_array = img_array.reshape((1, image_height, image_width, 3)) # Reshape to (1, 224, 224, 3)
#img_array = img_array.astype(np.float32) / 255.0 # Normalize pixel values
# Perform additional preprocessing if needed, e.g., center-cropping
# Make predictions using the loaded model
predictions = model.predict(img_array)
# Get the predicted class label
predicted_class_index = np.argmax(predictions)
predicted_class = class_names[predicted_class_index]
# Return the predicted class name for Gradio to display in the output
return predicted_class
# Create Gradio Interface
iface = gr.Interface(
fn=predict_image,
inputs="image", # Gradio will automatically create an image uploader
outputs="text", # Display the predicted class name as text
)
# Launch the Gradio Interface
iface.launch(share=True)
|