revert change on app.py

app.py

@@ -1,69 +1,66 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['modelname', 'pokemon_types', 'pokemon_types_en', 'examplespath', 'learn_inf', 'lang', 'prob_threshold',
+           'classify_image']
+
+# %% ../app.ipynb 3
 import pandas as pd
-from huggingface_hub import hf_hub_download
-from fastai.learner import load_learner
-from flask import Flask, request
-import gradio as gr
 
-# Charger le modèle et les données
-modelname = 'model_gen0.pkl'
-pokemon_types = pd.read_csv('pokemon.csv')
+modelname = f'model_gen0.pkl'
+pokemon_types = pd.read_csv(f'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))
 
-# Créer l'application Flask
-app = Flask(__name__)
+# %% ../app.ipynb 9
+import gradio as gr
 
-# Fonction de détection de la langue préférée du client
-def detect_language():
-    accept_language = request.headers.get("Accept-Language")
-    if accept_language:
-        languages = [lang.split(";")[0] for lang in accept_language.split(",")]
-        return languages[0]
-    return 'en'  # Par défaut, en anglais
+lang = 'en'
 
-# Route principale de l'application
-@app.route("/")
-def index():
-    lang = detect_language()
-    
-    # Définir le titre, la description et le libellé "inconnu" en fonction de la langue
-    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'
+prob_threshold = 0.75
 
-    # Fonction pour classifier l'image
-    def classify_image(img):
-        prob_threshold = 0.75
-        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
+from flask import request
+if request:
+    lang = request.headers.get("Accept-Language")
 
-    # Interface Gradio pour la classification d'image
-    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=examplespath, 
-                allow_flagging='auto')
+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():
+        interf = gr.Interface(
+            fn=classify_image, 
+            inputs=gr.inputs.Image(shape=(192,192)), 
+            outputs=gr.outputs.Label(), 
+            examples=examplespath, 
+            allow_flagging='auto')
 
-    return demo.launch(inline=False)
+demo.launch(inline=False)
 
-# Point d'entrée principal
-if __name__ == "__main__":
-    app.run()