adding updated app.py with loading model logic and requirements.txt

app.py

@@ -1,7 +1,23 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: sashimiIdentier.ipynb.
+
+# %% auto 0
+__all__ = ['learner', 'categories', 'img_examples', 'iface', 'sashimiIentifer']
+
+# %% sashimiIdentier.ipynb 19
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% sashimiIdentier.ipynb 20
+# first load the learner
+learner = load_learner ('./sashimiModel.pkl')
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# %% sashimiIdentier.ipynb 21
+# combine with gradio. 
+categories = ['Ahi(Yellowfin & Bigeye Tuna)', 'Ebi(Sweet Shrimp/Prawns)', 'Hamachi(Yellowtail)', 'Hamadai(Red Snapper)', 'Hotate(Scallops)', 'Ika(Squid)', 'Katsuo(Bonito/Skipjack Tuna)', 'Maguro(Bluefin Tuna)', 'Saba(Japanese Mackerel)', 'Sake(Salmon)']
+def sashimiIentifer(input_imgFilePath):
+    (pred,idx,probs) = learner.predict(input_imgFilePath)
+    return dict(zip(categories, map(float, probs)))
+  
+img_examples = ["Hotate_Scallops.jpg", "Ahi_YellowfinAndBigeyeTuna_Sashimi.jpg", "dalle_EngawaAndHotate.png"]
+iface = gr.Interface(fn=sashimiIentifer, inputs="image", outputs="label", examples =img_examples, title="Sashimi Identier", description="This app identifies which sashimi it is based on the given image")
 iface.launch()

requirements.txt

@@ -0,0 +1,2 @@
+fastai
+gradio