Create app.py

app.py

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+# %% [code] {"execution":{"iopub.status.busy":"2024-11-09T18:30:07.510515Z","iopub.execute_input":"2024-11-09T18:30:07.513741Z","iopub.status.idle":"2024-11-09T18:30:08.913485Z","shell.execute_reply.started":"2024-11-09T18:30:07.513509Z","shell.execute_reply":"2024-11-09T18:30:08.912208Z"}}
+# 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
+
+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))
+
+from fastai.vision.all import *
+!pip install gradio
+import gradio as gr
+
+share = True
+
+learn = load_learner('/kaggle/input/wastedetective/pytorch/default/1/model.pkl')
+
+categories = ('Batteries', 'Organic Matter', 'Primary', 'Tertiary')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+image = "image"
+label = "label"
+
+intf = gr.Interface(fn=classify_image, inputs = image, outputs = label)
+intf.launch(inline = False, share = True)