241 lines
6.2 KiB
Plaintext
241 lines
6.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['Buah Alpukat',\n",
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" 'Buah Apel',\n",
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" 'Buah Belimbing',\n",
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" 'Buah Naga',\n",
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" 'Buah Lemon',\n",
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" 'Buah Nanas',\n",
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" 'Buah Pir',\n",
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" 'Buah Pisang',\n",
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" 'Buah Salak',\n",
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" 'Buah Stroberi']"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastai.vision.all import *\n",
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"\n",
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"learn = load_learner('model.pkl')\n",
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"\n",
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"\n",
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"def format_name(name):\n",
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" if name != 'buah naga':\n",
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" return f'Buah {name.title()}'\n",
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" \n",
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" return name.title()\n",
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"\n",
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"\n",
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"names = list(map(format_name, learn.dls.vocab))\n",
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"names\n",
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"\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import firebase_admin\n",
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"from firebase_admin import credentials\n",
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"from firebase_admin import firestore\n",
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"\n",
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"cred = credentials.Certificate('firebase_key.json')\n",
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"app = firebase_admin.initialize_app(cred)\n",
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"db = firestore.client()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_ref = db.collection(u'preds').add({\n",
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" u'image': u'Lovelace',\n",
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" u'prediction': 1815,\n",
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" u'time_added': firestore.SERVER_TIMESTAMP\n",
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"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"preds_ref = db.collection(u'preds')\n",
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"docs = preds_ref.stream()\n",
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"\n",
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"for doc in docs:\n",
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" data = base64.b64decode(doc.to_dict()['image'])\n",
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" buff = BytesIO(data)\n",
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" img = Image.open(buff)\n",
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" img.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running on local URL: http://127.0.0.1:7860\n",
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"\n",
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"To create a public link, set `share=True` in `launch()`.\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"(<gradio.routes.App at 0x7f3279fb6af0>, 'http://127.0.0.1:7860/', None)"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"text/html": [
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"\n",
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"<style>\n",
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" /* Turns off some styling */\n",
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" progress {\n",
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" /* gets rid of default border in Firefox and Opera. */\n",
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" border: none;\n",
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" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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" background-size: auto;\n",
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" }\n",
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" progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
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" background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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" }\n",
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" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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" background: #F44336;\n",
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" }\n",
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"</style>\n"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import gradio as gr\n",
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"from fastai.vision.all import *\n",
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"import firebase_admin\n",
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"from firebase_admin import credentials\n",
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"from firebase_admin import firestore\n",
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"import base64\n",
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"from io import BytesIO\n",
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"\n",
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"cred = credentials.Certificate('firebase_key.json')\n",
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"app = firebase_admin.initialize_app(cred)\n",
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"db = firestore.client()\n",
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"\n",
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"learn = load_learner('model.pkl')\n",
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"names = list(map(lambda name: name.title(), learn.dls.vocab))\n",
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"\n",
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"\n",
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"def classify(image):\n",
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" buffered = BytesIO()\n",
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" image.save(buffered, format=\"JPEG\")\n",
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" img_str = base64.b64encode(buffered.getvalue())\n",
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"\n",
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" pred, idx, probs = learn.predict(np.asarray(image))\n",
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"\n",
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" db.collection(u'preds').add({\n",
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" u'image': img_str,\n",
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" u'prediction': pred.title(),\n",
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" u'time_added': firestore.SERVER_TIMESTAMP\n",
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" })\n",
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"\n",
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" return dict(zip(names, map(float, probs)))\n",
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"\n",
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"\n",
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"with gr.Blocks() as demo:\n",
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" with gr.Row():\n",
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" with gr.Column():\n",
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" image_input = gr.Image(label=\"Gambar\", shape=(200, 200), type='pil')\n",
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" predict_btn = gr.Button(\"Klasifikasi\", variant='primary')\n",
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" with gr.Column():\n",
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" chart = gr.Label(label=\"Hasil\")\n",
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"\n",
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" predict_btn.click(fn=classify, inputs=image_input,\n",
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" outputs=chart, api_name='classify_image')\n",
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"\n",
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"demo.launch()\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.12 ('base')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "fdff80770fe959a274e11f3f42c90636d375da6f3b1d0ebe5804fc741631cbf3"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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