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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) |