app.py

app.py

@@ -1,44 +1,61 @@
 #!/usr/bin/env python
+# coding: utf-8
 
 # In[12]:
 
+
 import gradio as gr
-from fastai.vision.all import *
+
 
 # In[13]:
 
 
+from fastai.vision.all import *
+
+
 # In[14]:
 
-learn = load_learner("model.pkl")
+
+learn = load_learner('model.pkl')
+
 
 # In[15]:
 
-categories = {"Ronaldo", "messi", "Michael Jordan", "Rafael Nadal", "Roger Federer"}
 
+categories = {'Ronaldo', 'messi', 'Michael Jordan', 'Rafael Nadal', 'Roger Federer'}
 
 def classify_athlete(img):
-	pred, idx, probs = learn.predict(img)
-	return dict(zip(categories, map(float, probs)))
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
 
 
 # In[21]:
 
-classify_athlete("cr7.jpeg")
 
-# In[24]:
+classify_athlete('cr7.jpeg')
+
+
+# In[26]:
 
-image = gr.inputs.Image(shape=(192, 192))
+
+image = gr.inputs.Image(shape = (192,192))
 label = gr.outputs.Label()
-examples = ["cr7.jpeg", "messi.jpeg", "michael_jordan.jpeg"]
+examples = ['cr7.jpeg','messi.jpeg','michael_jordan.jpeg']
+
+intf = gr.Interface(fn = classify_athlete,inputs = image, outputs = label, examples = examples)
+intf.launch(inline = False)
 
-intf = gr.Interface(fn=classify_athlete, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False, share=True)
 
 # # Making and exporting a python script
 
-# In[21]:
+# In[34]:
+
+
+get_ipython().system('jupyter nbconvert --to script app.ipynb')
 
-# Export Notebook to python Script -- nbdev --
 
 # In[ ]:
+
+
+
+