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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# ---------------- LOAD TRAINED MODEL ---------------- #
model = tf.keras.models.load_model("chest_xray_model.h5") # Make sure this file is uploaded
class_labels = ["Normal", "Pneumonia"]
# ---------------- PREDICTION FUNCTION ---------------- #
def predict_xray(img):
# Preprocess
img = img.resize((224, 224))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
# Model prediction
prediction = model.predict(img_array, verbose=0)[0][0]
label = class_labels[int(prediction > 0.5)]
confidence = prediction if prediction > 0.5 else 1 - prediction
# Detailed preliminary radiology report and first aid
if label == "Pneumonia":
report = (
"Preliminary Radiology Report:\n"
"- The chest X-ray shows opacities or infiltrates consistent with pneumonia.\n"
"- Findings suggest possible lung inflammation or infection.\n"
"- Further diagnostic tests (blood tests, sputum culture, oxygen saturation) recommended.\n\n"
"First Aid / Immediate Actions:\n"
"1. Seek medical attention immediately for confirmation and treatment.\n"
"2. Monitor for severe symptoms: high fever, shortness of breath, chest pain, confusion.\n"
"3. Ensure hydration and rest.\n"
"4. Avoid self-medicating with antibiotics without doctor supervision.\n"
"5. Use a mask and maintain good hygiene to prevent spread if contagious.\n"
"6. Keep a pulse oximeter if available; seek emergency care if oxygen saturation < 94%.\n"
"7. Note any worsening symptoms and report them to healthcare providers promptly.\n"
)
else:
report = (
"Preliminary Radiology Report:\n"
"- No visible signs of pneumonia detected on this X-ray.\n"
"- Lungs appear clear, but clinical correlation is advised.\n\n"
"General Advice:\n"
"1. Maintain healthy habits: good hydration, nutrition, and regular exercise.\n"
"2. Seek medical attention if respiratory symptoms develop.\n"
"3. Continue monitoring for cough, fever, or shortness of breath.\n"
)
return f"Prediction: {label} ({confidence*100:.2f}% confidence)\n\n{report}"
# ---------------- GRADIO INTERFACE ---------------- #
interface = gr.Interface(
fn=predict_xray,
inputs=gr.Image(type="pil"),
outputs="text",
title="Pneumonia Detector, Analyser and What to do",
description="Upload a chest X-ray to get a detailed preliminary report and first-aid recommendations."
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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