import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
import numpy as np
from tensorflow.keras.preprocessing import image

model = tf.keras.models.load_model('tumor_model.h5')

def predict_input_image(img):
    # Convert NumPy array to PIL.Image
    img = Image.fromarray(np.uint8(img))
    img = img.convert('RGB')
    img = img.resize((224, 224))
    img = np.array(img).reshape(1, 224, 224, 3)  # Reshape the image to match the model input

    # Make predictions
    prediction = model.predict(img)
    result = 'No Tumor Detected' if prediction[0][0] > 0.5 else 'Tumor detected'

    return f"Prediction: {result}"

# Define Gradio interface
iface = gr.Interface(
    fn=predict_input_image,
    inputs="image",
    outputs="text",
)

# Launch the interface
iface.launch()