app.py

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):
    # If no image is uploaded, return a message
    if img_path is None:
        return "Please upload an image.", fixed_image_url
    # 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)