import gradio as gr
import tensorflow as tf
from PIL import Image, ImageOps
import numpy as np

# Load the model
model = tf.keras.models.load_model("pneumonia_cnn_model.h5")

# Prediction function
def predict(image):
    try:
        # Convert to grayscale
        img = ImageOps.grayscale(image)
        
        # Resize image to match the model's expected input size (299x299)
        img = img.resize((299, 299))  # Resize to 299x299
        
        # Convert to numpy array and normalize the pixel values
        img_array = np.array(img).reshape(1, 299, 299, 1) / 255.0  # Reshape to (1, 299, 299, 1)
        
        # Make prediction
        prediction = model.predict(img_array)
        
        # Interpret prediction
        if prediction >= 0.5:
            return "Pneumonia detected"
        else:
            return "No pneumonia detected"
    except Exception as e:
        return f"Error during prediction: {str(e)}"

# Create the Gradio interface
iface = gr.Interface(
    fn=predict,  # Function to call on image input
    inputs=gr.Image(type="pil", label="Upload Chest X-ray Image"),
    outputs="text",  # Output is the prediction result (text)
    live=True  # Optional: set to False if you don't want to update results live
)

# Launch the app
iface.launch()