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

model_path = 'model'
model = tf.saved_model.load(model_path)

labels = ['butterfly', 'cats', 'cow', 'dogs', 'elephant',
          'horse', 'monkey', 'sheep', 'spider', 'squirrel']

def predict_image(image):
  image_resized = image.resize((224, 224))
  image_array = np.array(image_resized).astype(np.float32) / 255.0
  image_array = np.expand_dims(image_array, axis=0)

  predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']

  # Top 3 classes
  top_3_indices = np.argsort(predictions.numpy(), axis=1)[0][-3:][::-1]
  top_3_labels = [labels[i] for i in top_3_indices]
  top_3_probabilities = [predictions.numpy()[0][i] * 100 for i in top_3_indices]

  output_string = "\n".join([f"{label}: {probability:.2f}%" for label, probability in zip(top_3_labels, top_3_probabilities)])

  return image_resized, output_string

# Gradio Interface
interface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=[gr.Image(type="pil"), "text"],
    title="Animal Classifier",
    description="Upload an image of an animal, and the model will predict it."
)

interface.launch(share=True)