add title

app.py

@@ -1,42 +1,64 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../pokemonclassifier.ipynb.
 
 # %% auto 0
-__all__ = ['modelname', 'pokemon_types', 'pokemon_types_en', 'pokemon_types_fr', 'learn_inf', 'imagespath', 'image', 'label',
-           'examples', 'intf', 'classify_image']
+__all__ = ['modelname', 'pokemon_types', 'pokemon_types_en', 'learn_inf', 'imagespath', 'lang', 'prob_threshold',
+           'classify_image']
 
 # %% ../pokemonclassifier.ipynb 3
 import pandas as pd
 
 modelname = 'model.pkl'
 
-pokemon_types = pd.read_csv("pokemongen1patch.csv", nrows=20)
+pokemon_types = pd.read_csv("pokemongen1patch.csv")
 pokemon_types_en = pokemon_types['en']
-pokemon_types_fr = pokemon_types['fr']
 
-# %% ../pokemonclassifier.ipynb 24
+# %% ../pokemonclassifier.ipynb 27
 from huggingface_hub import hf_hub_download
 from fastai.learner import load_learner
 
 learn_inf = load_learner(hf_hub_download("Okkoman/PokeFace", modelname))
-learn_inf.dls.vocab
 imagespath = ''
 
-# %% ../pokemonclassifier.ipynb 29
+# %% ../pokemonclassifier.ipynb 31
 import gradio as gr
+from flask import request
 
+lang = 'en'
+prob_threshold = 0.75
+    
+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'
+    else:
+        title = "# PokeFace : What is this pokemon ?"
+        description = "## An image classifier for 1st generation pokemons (001-151)"
+        unknown = 'unknown'
+        
 def classify_image(img):
-    prob_threshold = 0.8
     pred,pred_idx,probs = learn_inf.predict(img)
     index = pokemon_types_en[pokemon_types_en == pred].index[0]
-    pred_fr = pokemon_types_fr.iloc[index]
+    label = pokemon_types[lang].iloc[index]
     if probs[pred_idx] > prob_threshold:
-        return f"{pred}(en) - {pred_fr}(fr) - {probs[pred_idx]*100:.0f}%"
+        return f"{label} {probs[pred_idx]*100:.0f}%"
     else:
-        return 'unknown'
+        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')
 
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = [f"{imagespath}pikachu.webp", f"{imagespath}bulbizarre.jpg", f"{imagespath}tortank.png"]
+demo.launch(inline=False)
 
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)

log.csv

@@ -0,0 +1,4 @@
+input
+pikachu.webp
+rhinocorne.webp
+bulbizarre.jpg

requirements.txt

@@ -1,3 +1,4 @@
+flask
 fastai>=2.0.0
 pandas