Commit : 107

app.py

@@ -1,54 +1,27 @@
-%pip install fastbook
+# AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified).
 
-import gradio as gr
-from fastbook import *
-
-
-Search_Words = ["bird", "forest"]
-path = Path("images")
-Search_Num = 10
-
-for W in Search_Words:
-  dest = path/W
-  dest.mkdir(exist_ok=True, parents=True)
-  download_images(dest, urls=search_images_ddg(f'{W} photo', max_images=Search_Num))
-  time.sleep(5)
-  resize_images(path/W, max_size=400, dest=path/W)
+__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'image', 'label', 'examples', 'intf']
 
+# Cell
+from fastai.vision.all import *
+import gradio as gr
 
-dls = DataBlock(
-    blocks=(ImageBlock, CategoryBlock),
-    get_items=get_image_files,
-    splitter=RandomSplitter(valid_pct=0.2, seed=42),
-    get_y=parent_label,
-    item_tfms=[Resize(192, method='squish')]
-).dataloaders(path, bs=6)
-
-
-learn = vision_learner(dls, resnet18, metrics=error_rate)
-learn.fine_tune(3)
-
-def predict_image(image):
-    img = PILImage.create(image)
-    pred, pred_idx, probs = learn.predict(img)
-    return pred
+def is_cat(x): return x[0].isupper()
 
-# Create a Gradio interface with updated input/output methods
-iface = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(),  # Use gr.Image for input
-    outputs=gr.Label(),   # Use gr.Label and gr.Number for output
-    examples=[
-        ["Examples/1.jpg"], 
-        ["Examples/2.jpg"], 
-        ["Examples/3.jpg"], 
-        ["Examples/4.jpg"]
-    ]
-)
+# Cell
+learn = load_learner('model.pkl')
 
-iface.launch()
+# Cell
+categories = ('Dog', 'Cat')
 
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
 
+# Cell
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
 
-demo = gr.Interface(fn=prediction, inputs=image, outputs=label, examples=examples)
-demo.launch(inline = False)
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)