first and final commit

classifier.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e0bddbd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -Uqq fastbook\n",
+    "import fastbook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1b46b67e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "from fastai.vision.widgets import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4a089247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "temp = pathlib.PosixPath\n",
+    "pathlib.PosixPath = pathlib.WindowsPath"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1f07c129",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = Path()\n",
+    "learn_inf = load_learner(path/'export.pkl', cpu=True)\n",
+    "btn_upload = widgets.FileUpload()\n",
+    "out_pl = widgets.Output()\n",
+    "lbl_pred = widgets.Label()\n",
+    "btn_run = widgets.Button(description='Classify')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b319ee30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def on_click_classify(change):\n",
+    "    img = PILImage.create(btn_upload.data[-1])\n",
+    "    out_pl.clear_output()\n",
+    "    with out_pl: display(img.to_thumb(128,128))\n",
+    "    pred, pred_idx, probs = learn_inf.predict(img)\n",
+    "    lbl_pred.value = f'Prediction: {str(pred)[10:]}; Probability: {probs[pred_idx]:.04f}'\n",
+    "    \n",
+    "btn_run.on_click(on_click_classify)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "114b5a71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "476e154bfede489a9560404bca8fff16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='Select your images'), FileUpload(value={}, description='Upload'), Button(descripti…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "VBox([widgets.Label('Select your images'),\n",
+    "     btn_upload, btn_run, out_pl, lbl_pred])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d640937e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "d925690840644bcd766647b280b19c370132b1426b7601e4e2f280a77c18a034"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

classifier.py

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+import streamlit as st 
+from fastai.vision.all import *
+from PIL import Image
+import pathlib
+import urllib.request
+
+temp = pathlib.PosixPath
+plt = platform.system()
+if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
+
+# MODEL_URL = "https://drive.google.com/uc?export=download&id=1cH5nY1T5oykEcLyWtjA8Wv-Xn8vO20BS"
+# urllib.request.urlretrieve(MODEL_URL, "model.pkl")
+
+path = Path()
+path.ls(file_exts='.pkl')
+
+learn_inf = load_learner(path/'export.pkl', cpu=True)
+# out_pl = st.image(load_image(image), width=250)
+
+
+def load_image(img_file):
+    img = PILImage.create(img_file)
+    return img
+
+def on_click_classify(image):
+    # load_image(image)
+    out_pl = st.image(load_image(image), width=250)
+    pred, pred_idx, probs = learn_inf.predict(load_image(image))
+    st.write('Prediction: ', str(pred)[10:], '; Probability: ', float(probs[pred_idx]))
+    
+
+st.title('Dog Classifier')
+st.header('Choose your Dog!!')
+image = st.file_uploader(label=' ', type=['png', 'jpg'], key='img', help='upload an img of dog')
+
+# picture = st.camera_input(label='Click your dog!')
+
+
+# btn_run.on_change(on_click_classify)
+btn_run = st.button(label='Classify')
+if btn_run:
+    on_click_classify(image)
+    
+    
+st.markdown('#### Created by **Umang Kaushik**')
+st.markdown('##### **[Github](https://github.com/Umang-10)**')
+

export.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:edf8e8bca20c151ae9b5787c8d1b6c3f3119d7a1c5ef5e1a6a7995c98d55ac60
+size 88116257

requirements.txt

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+-f https://download.pytorch.org/whl/torch_stable.html
+torch==1.11.0+cpu 
+torchvision==0.12.0+cpu
+fastai>=2.3.1
+streamlit
+pathlib