Upload app.py

app.py

@@ -1,14 +1,11 @@
-# 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
 
-modelname = f'model_gen0.pkl'
-pokemon_types = pd.read_csv(f'pokemon.csv')
+modelname = 'model_gen0.pkl'
+pokemon_types = pd.read_csv('pokemon.csv')
 pokemon_types_en = pokemon_types['en']
 
 examplespath = 'images/'
@@ -40,7 +37,7 @@ else:
     unknown = 'unknown'    
         
 def classify_image(img):
-    pred,pred_idx,probs = learn_inf.predict(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:
@@ -55,12 +52,14 @@ with gr.Blocks() as demo:
     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')
+        image_input = gr.Image(label="Upload an image", shape=(192, 192))
+        label_output = gr.Label(label="Prediction")
+        submit_button = gr.Button("Classify")
+        
+    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)