__all__ = ['modelname', 'pokemon_types', 'pokemon_types_en', 'examplespath', 'learn_inf', 'lang', 'prob_threshold',
           'classify_image']

# %% ../app.ipynb 3
import pandas as pd

modelname = 'model_gen0.pkl'
pokemon_types = pd.read_csv('pokemon.csv')
pokemon_types_en = pokemon_types['en']

examplespath = 'images/'

# %% ../app.ipynb 7
from huggingface_hub import hf_hub_download
from fastai.learner import load_learner

learn_inf = load_learner(hf_hub_download("Okkoman/PokeFace", modelname))

# %% ../app.ipynb 9
import gradio as gr

lang = 'en'

prob_threshold = 0.75

from flask import request
if request:
    lang = request.headers.get("Accept-Language")

if lang == 'fr':
    title = "# PokeFace - Quel est ce pokemon ?"
    description = "## Un classifieur pour les pokemons de 1ere et 2eme générations (001-251)"
    unknown = 'inconnu'
else:
    title = "# PokeFace - What is this pokemon ?"
    description = "## An classifier for 1st-2nd generation pokemons (001-251)"
    unknown = 'unknown'    
        
def classify_image(img):
    pred, pred_idx, probs = learn_inf.predict(img)
    index = pokemon_types_en[pokemon_types_en == pred].index[0]
    label = pokemon_types[lang].iloc[index]
    if probs[pred_idx] > prob_threshold:
        return f"{index+1} - {label} ({probs[pred_idx]*100:.0f}%)"
    else:
        return unknown
    
with gr.Blocks() as demo:
    
    with gr.Row():
        gr.Markdown(title)
    with gr.Row():
        gr.Markdown(description)        
    with gr.Row():
        image_input = gr.Image(label="Upload an image", width=192, height=192)
        submit_button = gr.Button("Classify")        
        label_output = gr.Label(label="Prediction")
    with gr.Row():
        gr.Examples(examples=examplespath, inputs=image_input)
        
    submit_button.click(fn=classify_image, inputs=image_input, outputs=label_output)

demo.launch(inline=False)