feat: working now

activate

@@ -0,0 +1 @@
+micromamba activate base

app.py

@@ -1,12 +1,4 @@
-# import gradio as gr
-
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
-
-__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
+__all__ = ['cloud_learn', 'classify_image', 'cloud_categories', 'cloud_examples', 'intf']
 
 import gradio as gr
 from fastai.vision.all import *
@@ -29,23 +21,18 @@ cloud_categories = (
   'Lenticular'
 )
 
-cloud_examples = [image_path / f"{c}.jpg" for c in cloud_categories]
-
-cloud_learner = load_learner(model_path/'cloudmodel.pkl')
+cloud_examples = [str(image_path / f"{c}.jpg") for c in cloud_categories]
+cloud_learn = load_learner(model_path/'cloudmodel.pkl')
 
-def is_cat(x): return x[0].isupper()
-
-learn = load_learner('model.pkl')
-
-categories = ('Dog', 'Cat')
 
 def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float,probs)))
+    pred, idx, probs = cloud_learn.predict(img)
+    return dict(zip(cloud_categories, map(float,probs)))
+
 
-image= gr.inputs.Image(shape=(192, 192))
-label = gr.outputs.Label()
-examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
+intf = gr.Interface(fn=classify_image, 
+    inputs=gr.inputs.Image(shape=(192, 192)), 
+    outputs=gr.outputs.Label(), 
+    examples=cloud_examples)
 
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
 intf.launch(inline=False)

models/cloudmodel.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:406827942d140e486ec8b53afd3f0376d34d9045dbae151e7af1d22442448ff1
-size 102978463
+oid sha256:5db5c0bdfef2a1f7f6883b5f853fe6b330c93a17dee1fa4043272a85bce3a678
+size 103263967