jhfj

README.md

@@ -1,7 +1,7 @@
 ---
 title: Multilabel Classification
 sdk: gradio
-app_file: petdector/core.py
+app_file: app.y
 pinned: false
 ---
 

app.py

@@ -1,17 +1,27 @@
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../00_core.ipynb.
+
+# %% auto 0
+__all__ = ['learn_inf', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../00_core.ipynb 3
 from fastai.vision.all import *
 
-def is_cat(x): return x[0].isupper() 
-categories = ('cat', 'dog')
-learn = load_learner('model.pkl')
+# %% ../00_core.ipynb 17
+learn_inf = load_learner('export.pkl')
 
+# %% ../00_core.ipynb 21
+import gradio as gr
+categories = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 
+              'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 
+              'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 
+              'sofa', 'train', 'tvmonitor']
 def classify_image(img):
-    pred, idx, probs = learn.predict(img)
+    red, idx, probs = learn_inf.predict(img)
     return dict(zip(categories, map(float, probs)))
 
-image = gr.inputs.Image(shape=(192, 192))
+image = gr.inputs.Image(shape=(192,192))
 label = gr.outputs.Label()
-examples = []
+examples = ['sheep.PNG', 'chair_diningtable.PNG', 'car_person.PNG']
 
-iface = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-iface.launch(inline=False)
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)