app.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import gradio as gr
from gradio.themes.base import Base
import cv2
from tensorflow.keras.models import load_model
model_path = "xray_model.h5"
model = load_model(model_path)
def load_and_prepare_image(image_path):
    # Load the image
    img = cv2.imread(image_path)
    # Resize the image to the input shape of the model
    img = cv2.resize(img, (256, 256))
    # Normalize the image
    img = img / 255.0
    # Expand dimensions to match the input shape of the model
    img = np.expand_dims(img, axis=0)
    return img

def predict(image_path):
    if image_path is None:
        return "Please upload an image.", "How-AI-is-Used-in-Healthcare.png"

    # Prepare the image
    img = load_and_prepare_image(image_path)

    # Make the prediction
    prediction = model.predict(img)

    # Convert prediction to descriptive sentence
    label = (
        'The X-ray indicates that the patient likely has pneumonia.'
        if prediction[0][0] > 0.9
        else 'The X-ray indicates that the patient\'s lungs are normal.'
    )

    return label, "How-AI-is-Used-in-Healthcare.png"

# Fixed image URL
fixed_image_url = "How-AI-is-Used-in-Healthcare.png"

# Example images and their descriptions
examples = [
    ["normal.jpeg", "Normal X-ray image."],
    ["pne.jpeg", "X-ray image indicating pneumonia."]
]
# Create the Gradio interface
iface = gr.Interface(
    theme=gr.themes.Soft(),
    fn=predict,
    inputs=gr.Image(type="filepath", label="Upload Image"),
    outputs=[
        gr.Textbox(label="Prediction"),
        gr.Image(value=fixed_image_url, label="Always ready to assist!")  # Display the fixed image
    ],
    title="Pneumonia Classification",
    description="Please upload a chest X-ray image.",
    examples=examples,
)
# Launch the interface
iface.launch(share=True)