from fastai.vision.all import *
import gradio as gr
learner = load_learner('export.pkl')

title = "Bird Species Classifier"
description = "A bird species classifier trained on the BIRDS 400 dataset from Kaggle with fastai. Created as a demo for Gradio and HuggingFace Spaces."
labels = learner.dls.vocab

def predict(img):
  img = PILImage.create(img)
  pred, pred_idx, probs = learner.predict(img)
  return {labels[i]: float(probs[i]) for i in range(len(labels))}


examples = ['1.jpg', '2.jpg', '3.jpg', '4.jpg', '5.jpg', '6.jpg', '7.jpg', '8.jpg', '9.jpg', '10.jpg', '11.jpg', '12.jpg', '13.jpg', '14.jpg', '15.jpg']


gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(224, 224)), outputs=gr.outputs.Label(num_top_classes=3), title=title, description=description, examples=examples).launch(share=True, debug=True)