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import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
from PIL import Image
# Fixed image URL
fixed_image_url = "222.PNG"
# Example images and their descriptions
examples = [
["Avulsion fracture.jpg", "Avulsion fracture."]
]
# Load your trained model
model = load_model("bone_break_classification_model.h5")
# Define your class names dictionary
class_names_dict = {
0: 'Avulsion fracture',
1: 'Comminuted fracture',
2: 'Fracture Dislocation',
3: 'Greenstick fracture',
4: 'Hairline Fracture',
5: 'Imapacted fracture',
6: 'Longitudinal fracture',
7: 'Oblique fracture',
8: 'Pathological fracture',
9: 'Spiral Fracture'
}
def predict_image(img_path):
# Load the image using PIL
img = Image.open(img_path)
img = img.resize((256, 256)) # Resize the image
img_array = np.array(img) / 255.0 # Normalize
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
# Make prediction
prediction = model.predict(img_array)
predicted_class_index = np.argmax(prediction, axis=-1)[0]
predicted_class_name = class_names_dict.get(predicted_class_index, "Unknown Class")
return predicted_class_name ,fixed_image_url
# Create Gradio interface
iface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="filepath", label="Upload an Image"),
outputs=[
gr.Textbox(label="Prediction"),
gr.Image(label=" Bone Fracture Detection ", value=fixed_image_url)
],
title="Bone Break Classification",
description=(
"Upload an X-ray image, and the model will predict the type of bone break. \n\n"
"Classes Available:\n"
"Avulsion fracture\n"
"Comminuted fracture\n"
"Fracture Dislocation\n"
"Greenstick fracture\n"
"Hairline Fracture\n"
"Impacted fracture\n"
"Longitudinal fracture\n"
"Oblique fracture\n"
"Pathological fracture\n"
"Spiral Fracture"
),
,
theme="ParityError/Interstellar",
examples=examples,
)
# Launch the interface
iface.launch(debug=True) |