First Commit

app.py

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+import pathlib
+import gradio as gr
+from fastai.vision.all import *
+
+# to solve the mismatch between windows (local) system and huggingspace (linux)
+plt = platform.system()
+if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
+
+learn = load_learner('export.pkl')
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+iface = gr.Interface(
+    fn=predict, 
+    inputs=gr.components.Image(shape=(512, 512)), 
+    outputs=gr.components.Label(num_top_classes=3),
+    description="Pet Classifier",
+    article="<p style='text-align: center'><a href='google.com' target='_blank'>Blog post</a></p>",
+    live=True,    
+    )
+
+iface.launch(enable_queue=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a700e65e0aaa01d0b1882387185378a6e2f614443e1d587b9a4a8a0ab753e273
+size 201292933

requirements.txt

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+fastai
+torch
+gradio
+numpy
+pandas
+timm

train.py

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+import fastai
+from fastai.vision.all import *
+import timm
+from PIL import Image
+from pathlib import Path
+from os import path
+from tqdm.auto import tqdm
+from urllib.error import HTTPError, URLError
+
+def search_images_ddg(term, max_images=200):
+    "Search for `term` with DuckDuckGo and return a unique urls of about `max_images` images"
+    assert max_images<1000
+    url = 'https://duckduckgo.com/'
+    res = urlread(url,data={'q':term})
+    searchObj = re.search(r'vqd=([\d-]+)\&', res)
+    assert searchObj
+    requestUrl = url + 'i.js'
+    params = dict(l='us-en', o='json', q=term, vqd=searchObj.group(1), f=',,,', p='1', v7exp='a')
+    urls,data = set(),{'next':1}
+    headers = dict(referer='https://duckduckgo.com/')
+    while len(urls)<max_images and 'next' in data:
+        try:
+            res = urlread(requestUrl, data=params, headers=headers)
+            data = json.loads(res) if res else {}
+            urls.update(L(data['results']).itemgot('image'))
+            requestUrl = url + data['next']
+        except (URLError,HTTPError): pass
+        time.sleep(1)
+    return L(urls)[:max_images]
+
+tool_names = "resistor", "bipolar transistor", "mosfet", "capacitor", "inductor", "wire", "led", "diode", "thermistor", "switch", "battery", "hammer", "screwdriver", "scissors", "wrench", "mallet", "axe"
+path = Path("data", "tools")
+path.absolute()
+
+
+if not path.exists():
+    path.mkdir(parents=True)
+    for o in tqdm(tool_names):
+        dest = (path/o)
+        dest.mkdir(exist_ok=True)
+        results = search_images_ddg(f'{o}', max_images=20)
+        download_images(dest, urls=results, n_workers=2)
+
+fns = get_image_files(path)
+failed = verify_images(fns)
+failed.map(Path.unlink)
+
+data_config = DataBlock(
+    blocks=(ImageBlock, CategoryBlock),
+    get_items=get_image_files,
+    splitter=RandomSplitter(seed=42),
+    get_y=parent_label,
+    item_tfms=Resize(224)
+)
+
+dls = data_config.dataloaders(path)
+connectors = data_config.new(item_tfms=RandomResizedCrop(224, min_scale=0.5), batch_tfms=aug_transforms())
+dls = connectors.dataloaders(path)
+
+learn = vision_learner(dls, 'convnext_small.fb_in22k_ft_in1k', metrics=error_rate)
+learn.fine_tune(7, freeze_epochs=1)
+learn.path = Path('.')
+learn.export()