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__all__ = ['modelname', 'pokemon_types', 'pokemon_types_en', 'examplespath', 'learn_inf', 'lang', 'prob_threshold', |
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'classify_image'] |
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import pandas as pd |
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modelname = f'model_gen0.pkl' |
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pokemon_types = pd.read_csv(f'pokemon.csv') |
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pokemon_types_en = pokemon_types['en'] |
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examplespath = 'images/' |
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from huggingface_hub import hf_hub_download |
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from fastai.learner import load_learner |
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learn_inf = load_learner(hf_hub_download("Okkoman/PokeFace", modelname)) |
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import gradio as gr |
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lang = 'en' |
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prob_threshold = 0.75 |
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from flask import request |
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if request: |
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lang = request.headers.get("Accept-Language") |
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if lang == 'fr': |
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title = "# PokeFace - Quel est ce pokemon ?" |
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description = "## Un classifieur pour les pokemons de 1ere et 2eme générations (001-251)" |
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unknown = 'inconnu' |
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else: |
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title = "# PokeFace - What is this pokemon ?" |
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description = "## An classifier for 1st-2nd generation pokemons (001-251)" |
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unknown = 'unknown' |
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def classify_image(img): |
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pred,pred_idx,probs = learn_inf.predict(img) |
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index = pokemon_types_en[pokemon_types_en == pred].index[0] |
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label = pokemon_types[lang].iloc[index] |
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if probs[pred_idx] > prob_threshold: |
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return f"{index+1} - {label} ({probs[pred_idx]*100:.0f}%)" |
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else: |
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return unknown |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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gr.Markdown(title) |
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with gr.Row(): |
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gr.Markdown(description) |
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with gr.Row(): |
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interf = gr.Interface( |
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fn=classify_image, |
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inputs=gr.inputs.Image(shape=(192,192)), |
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outputs=gr.outputs.Label(), |
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examples=examplespath, |
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allow_flagging='auto') |
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demo.launch(inline=False) |
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