Update app.py

app.py

@@ -1,41 +1,37 @@
 import gradio as gr
-
 from duckduckgo_search import DDGS
 from fastcore.all import *
+from fastdownload import download_url
+from fastai.vision.all import *
+from PIL import Image
+from pathlib import Path
 
+# Define search function using DuckDuckGo
 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]
+# Create a folder for storing images
+path = Path('images')
+path.mkdir(exist_ok=True)
 
+# Download example image: beaver
+beaver_url = search_images('beaver photo', max_images=1)[0]
+download_url(beaver_url, path/'beaver.jpg', show_progress=False)
 
+# Download another example image: platypus
+platypus_url = search_images('platypus photo', max_images=1)[0]
+download_url(platypus_url, path/'platypus.jpg', show_progress=False)
 
-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)
-
+# Show a thumbnail of the platypus image
+Image.open(path/'platypus.jpg').thumbnail((256,256))
 
+# Remove any corrupt images
 failed = verify_images(get_image_files(path))
 failed.map(Path.unlink)
-len(failed)
-
 
+# Prepare DataLoaders (make sure `images/` has subfolders of labeled images)
 dls = DataBlock(
     blocks=(ImageBlock, CategoryBlock), 
     get_items=get_image_files, 
@@ -44,16 +40,19 @@ dls = DataBlock(
     item_tfms=[Resize(192, method='squish')]
 ).dataloaders(path, bs=32)
 
+# Show a sample batch
 dls.show_batch(max_n=6)
 
-
+# Train a model
 learn = vision_learner(dls, resnet18, metrics=error_rate)
 learn.fine_tune(3)
 
+# Prediction function
+def predict_species(img):
+    is_sheep, _, probs = learn.predict(img)
+    return f"This looks like a: {is_sheep}. Probability it's a beaver: {probs[0]:.4f}"
 
-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")
+# Define Gradio interface
+demo = gr.Interface(fn=predict_species, inputs=gr.Image(type="pil"), outputs="text")
 demo.launch()
+