Create content

app.py

@@ -1,7 +1,62 @@
 import gradio as gr
+from fastbook import *
+from fastai.vision.widgets import *
+from fastcore.all import *
+from urllib.error import URLError,HTTPError
+import json
+import matplotlib as mpl, pkgutil, requests, time
 
-def greet(name):
-    return "Hello " + name + "!!"
+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]
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+btn_upload = widgets.FileUpload()
+btn_upload
+
+img = PILImage.create(btn_upload.data[-1])
+img
+
+out_pl = widgets.Output()
+out_pl.clear_output()
+with out_pl: display(img.to_thumb(128,128))
+out_pl
+
+pred,pred_idx,probs = learn_inf.predict(img)
+
+lbl_pred = widgets.Label()
+lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+lbl_pred
+
+btn_run = widgets.Button(description='Classify')
+btn_run
+
+def on_click_classify(change):
+    img = PILImage.create(btn_upload.data[-1])
+    out_pl.clear_output()
+    with out_pl: display(img.to_thumb(128,128))
+    pred,pred_idx,probs = learn_inf.predict(img)
+    lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+
+btn_run.on_click(on_click_classify)
+
+btn_upload = widgets.FileUpload()
+
+VBox([widgets.Label('Select your bear!'), 
+      btn_upload, btn_run, out_pl, lbl_pred])

export.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb16d427d20e5ed16d133a595707da69ef882e33dad6a5918a529e8faffa4762
+size 46973374