added app file

anime_image_label_inference.py

@@ -1,23 +1,8 @@
-# -*- coding: utf-8 -*-
-"""Anime Image Label Inference.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1BxPfM2uV54LeiQGEk43xcyNwMgqwCR80
-"""
-
-!pip install -Uqq gradio
-
-!pip install -Uqq fastbook
+#import gradio as gr
 import fastbook
 fastbook.setup_book()
-
-import gradio as gr
 from fastbook import *
 
-path = Path('gdrive/MyDrive/anime-image-labeller/safebooru')
-
 """
 Get the prediction labels and their accuracies, then return the results as a dictionary.
 
@@ -28,7 +13,7 @@ Get the prediction labels and their accuracies, then return the results as a dic
 def get_pred_classes(obj, learn, thresh):
     labels = []
     # get list of classes from csv--replace
-    with open(path/'classes.txt', 'r') as f:
+    with open('classes.txt', 'r') as f:
       for line in f:
         labels.append(line.strip('\n'))
 
@@ -44,10 +29,10 @@ def get_pred_classes(obj, learn, thresh):
 
     return predictions
 
-def get_x(r): return path/'images'/r['img_name']
+def get_x(r): return 'images'/r['img_name']
 def get_y(r): return [t for t in r['tags'].split(' ') if t in pop_tags]
 
-learn = load_learner(path/'model-large-40e.pkl')
+learn = load_learner('model-large-40e.pkl')
 
 def predict_single_img(imf, thresh=0.2, learn=learn):
   
@@ -58,9 +43,11 @@ def predict_single_img(imf, thresh=0.2, learn=learn):
   img.show() #show image
   return str(get_pred_classes(pred_pct, learn, thresh))
 
-predict_single_img(path/'test/mask.jpeg')
+predict_single_img('test/mask.jpeg')
 
+"""
 iface = gr.Interface(fn=predict_single_img, 
                      inputs=["image","number"], 
                      outputs="text")
-iface.launch()
+iface.launch()
+"""

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import fastbook
+fastbook.setup_book()
+from fastbook import *
+
+"""
+Get the prediction labels and their accuracies, then return the results as a dictionary.
+
+[obj] - tensor matrix containing the predicted accuracy given from the model
+[learn] - fastai learner needed to get the labels
+[thresh] - minimum accuracy threshold to returning results
+"""
+def get_pred_classes(obj, learn, thresh):
+    labels = []
+    # get list of classes from csv--replace
+    with open('classes.txt', 'r') as f:
+      for line in f:
+        labels.append(line.strip('\n'))
+
+    predictions = {}
+    x=0
+    for item in obj:
+        acc= round(item.item(), 3)
+        if acc > thresh:
+            predictions[labels[x]] = round(acc, 3)
+        x+=1
+
+    predictions =sorted(predictions.items(), key=lambda x: x[1], reverse=True)
+
+    return predictions
+
+def get_x(r): return 'images'/r['img_name']
+def get_y(r): return [t for t in r['tags'].split(' ') if t in pop_tags]
+
+learn = load_learner(fname='model-large-40e.pkl')
+
+def predict_single_img(imf, thresh=0.2, learn=learn):
+  
+  img = PILImage.create(imf)
+
+  #img.show() #show image
+  _, _, pred_pct = learn.predict(img) #predict while ignoring first 2 array inputs
+  img.show() #show image
+  return str(get_pred_classes(pred_pct, learn, thresh))
+
+#predict_single_img('test/mask.jpeg')
+
+iface = gr.Interface(fn=predict_single_img, 
+                     inputs=["image","number"], 
+                     outputs="text")
+iface.launch()