Upload app.py

app.py

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+# %% [code] {"execution":{"iopub.status.busy":"2023-07-31T16:51:35.137587Z","iopub.execute_input":"2023-07-31T16:51:35.137972Z","iopub.status.idle":"2023-07-31T16:51:49.983937Z","shell.execute_reply.started":"2023-07-31T16:51:35.137942Z","shell.execute_reply":"2023-07-31T16:51:49.982981Z"}}
+# This Python 3 environment comes with many helpful analytics libraries installed
+# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
+# For example, here's several helpful packages to load
+
+!pip install gradio
+
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+
+# Input data files are available in the read-only "../input/" directory
+# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
+
+import os
+for dirname, _, filenames in os.walk('/kaggle/input'):
+    for filename in filenames:
+        print(os.path.join(dirname, filename))
+
+# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
+# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
+
+# %% [code] {"execution":{"iopub.status.busy":"2023-07-31T16:52:23.519405Z","iopub.execute_input":"2023-07-31T16:52:23.519821Z","iopub.status.idle":"2023-07-31T16:52:30.470389Z","shell.execute_reply.started":"2023-07-31T16:52:23.519792Z","shell.execute_reply":"2023-07-31T16:52:30.469705Z"}}
+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('/kaggle/input/bicycle-model/model.pkl')
+
+# %% [code] {"execution":{"iopub.status.busy":"2023-07-31T16:52:36.499387Z","iopub.execute_input":"2023-07-31T16:52:36.499737Z","iopub.status.idle":"2023-07-31T16:52:36.506774Z","shell.execute_reply.started":"2023-07-31T16:52:36.499713Z","shell.execute_reply":"2023-07-31T16:52:36.505348Z"}}
+labels = learn.dls.vocab
+
+def predict(img):
+    img = PILImage.create(img)
+    pred, idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# %% [code] {"execution":{"iopub.status.busy":"2023-07-31T17:01:15.166908Z","iopub.execute_input":"2023-07-31T17:01:15.168263Z","iopub.status.idle":"2023-07-31T17:01:25.050227Z","shell.execute_reply.started":"2023-07-31T17:01:15.168215Z","shell.execute_reply":"2023-07-31T17:01:25.049147Z"}}
+examples = ['/kaggle/input/examples/street_bicycle.jpg',
+           '/kaggle/input/examples/street_image.jpg']
+
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)),
+             title = "bicycle detector",
+             examples = examples,
+             interpretation = 'default',
+             enable_queue = True,
+             outputs = gr.outputs.Label(num_top_classes=3)).launch(share=True),