app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: ../pokemonclassifier.ipynb.

# %% auto 0
__all__ = ['modelname', 'imagespath', 'pokemon_types', 'pokemon_types_en', 'learn_inf', 'lang', 'title', 'description', 'unknown',
           'prob_threshold', 'classify_image']

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

modelname = 'model.pkl'
imagespath = 'images/'

pokemon_types = pd.read_csv("pokemongen1patch.csv")
pokemon_types_en = pokemon_types['en']

# %% ../pokemonclassifier.ipynb 27
from huggingface_hub import hf_hub_download
from fastai.learner import load_learner

learn_inf = load_learner(hf_hub_download("Okkoman/PokeFace151", modelname))

# %% ../pokemonclassifier.ipynb 30
import gradio as gr

lang = 'en'
title = "# PokeFace - What is this pokemon ?"
description = "## An image classifier for 1st generation pokemons (001-151)"
unknown = 'unknown'

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 d'images pour les pokemons de 1ere génération (001-151)"
        unknown = 'inconnu'
        
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"{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():
        interf = gr.Interface(
            fn=classify_image, 
            inputs=gr.inputs.Image(shape=(192,192)), 
            outputs=gr.outputs.Label(), 
            examples=imagespath, 
            allow_flagging='auto')

demo.launch(inline=False)