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

interpreter = tf.lite.Interpreter(model_path="leaf_model_85_percent.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

def classify(image):
    img = image.resize((224, 224)).convert("RGB")
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0).astype(np.float32)

    interpreter.set_tensor(input_details[0]['index'], img_array)
    interpreter.invoke()
    output = interpreter.get_tensor(output_details[0]['index'])[0][0]
    return "Unhealthy" if output > 0.45 else "Healthy"

iface = gr.Interface(fn=classify, inputs=gr.Image(type="pil"), outputs="text")
iface.launch(debug=True, share=True)