Update app.py

app.py

@@ -1,7 +1,58 @@
 import gradio as gr
+from duckduckgo_search import DDGS
+from fastcore.all import *
 
-def greet(name):
-    return "Hello " + name + "!!"
+ddgs = DDGS()
 
+def search_images(term, max_images=30):
+    print(f"Searching for '{term}'")
+    return L(ddgs.images(term, max_results=max_images)).itemgot('image')
+
+#NB: `search_images` depends on duckduckgo.com, which doesn't always return correct responses.
+#    If you get a JSON error, just try running it again (it may take a couple of tries).
+urls = search_images('beaver photos', max_images=1)
+urls[0]
+
+
+
+from fastdownload import download_url
+dest = 'beaver.jpg'
+download_url(urls[0], dest, show_progress=False)
+
+from fastai.vision.all import *
+im = Image.open(dest)
+im.to_thumb(256,256)
+
+download_url(search_images('platypus photo', max_images=1)[0], 'platypus.jpg', show_progress=False)
+Image.open('platypus.jpg').to_thumb(256,256)
+
+download_url(search_images('platypus photo', max_images=1)[0], 'platypus.jpg', show_progress=False)
+Image.open('platypus.jpg').to_thumb(256,256)
+
+
+failed = verify_images(get_image_files(path))
+failed.map(Path.unlink)
+len(failed)
+
+
+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=32)
+
+dls.show_batch(max_n=6)
+
+
+learn = vision_learner(dls, resnet18, metrics=error_rate)
+learn.fine_tune(3)
+
+
+is_sheep,_,probs = learn.predict(PILImage.create('beaver.jpg'))
+print(f"This is a: {is_sheep}.")
+print(f"Probability it's a beaver: {probs[0]:.4f}")
+    
 demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()