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‎ClassExamples/ON47/Forest-Fire-Prediction/.ipynb_checkpoints/Predict_Forest_Fire-checkpoint.ipynb‎

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{
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"cells": [
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"import pickle\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")"
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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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"data = pd.read_csv(\"Forest_fire.csv\")\n",
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"data = np.array(data)\n",
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"print(data[:5])"
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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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"X = data[1:, 1:-1]\n",
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"y = data[1:, -1]\n",
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"y = y.astype('int')\n",
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"X = X.astype('int')\n",
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"print(len(X))\n",
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"print(X[:5],y[:5])\n"
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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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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
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"print(len(X_train) , len(X_test))\n",
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"print(len(y_train) , len(y_test))"
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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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"log_reg = LogisticRegression()\n",
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"\n",
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"log_reg.fit(X_train, y_train)"
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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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"inputt=[int(x) for x in \"45 32 60\".split(' ')]\n",
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"final=[np.array(inputt)]\n",
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"print(final)\n",
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"b = log_reg.predict_proba(final)\n",
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"print(b)\n",
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"\n",
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"pickle.dump(log_reg,open('model.pkl','wb'))\n",
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"model=pickle.load(open('model.pkl','rb'))"
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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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"#inputt=[int(x) for x in \"45 32 60\".split(' ')]\n",
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"#final=[np.array(inputt)]\n",
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"#print(final)\n",
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"b = log_reg.predict([[45,32,60]])\n",
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"print(b)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## use the model and display in Flask"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:42] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:42] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:42] \"\u001b[33mGET /.js/materialize.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:42] \"\u001b[33mGET /js/init.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:42] \"\u001b[33mGET /.js/materialize.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:42] \"\u001b[33mGET /js/init.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:50] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:50] \"\u001b[33mGET /js/init.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:50] \"\u001b[33mGET /.js/materialize.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:50] \"\u001b[33mGET /.js/materialize.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:20:50] \"\u001b[33mGET /js/init.js HTTP/1.1\u001b[0m\" 404 -\n"
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]
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},
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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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"[40, 40, 60]\n",
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"[array([40, 40, 60])]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[2020-08-27 16:21:02,633] ERROR in app: Exception on /predict [POST]\n",
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"Traceback (most recent call last):\n",
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" File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\flask\\app.py\", line 1982, in wsgi_app\n",
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" response = self.full_dispatch_request()\n",
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" File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\flask\\app.py\", line 1614, in full_dispatch_request\n",
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" rv = self.handle_user_exception(e)\n",
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" File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\flask\\app.py\", line 1517, in handle_user_exception\n",
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" reraise(exc_type, exc_value, tb)\n",
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" File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\flask\\_compat.py\", line 33, in reraise\n",
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" raise value\n",
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" File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\flask\\app.py\", line 1612, in full_dispatch_request\n",
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" rv = self.dispatch_request()\n",
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" File \"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\flask\\app.py\", line 1598, in dispatch_request\n",
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" return self.view_functions[rule.endpoint](**req.view_args)\n",
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" File \"<ipython-input-1-60d99ab23a5c>\", line 15, in predict\n",
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" int_features=[int(x) for x in request.form.values()]\n",
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" File \"<ipython-input-1-60d99ab23a5c>\", line 15, in <listcomp>\n",
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" int_features=[int(x) for x in request.form.values()]\n",
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"ValueError: invalid literal for int() with base 10: ''\n",
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"127.0.0.1 - - [27/Aug/2020 16:21:02] \"\u001b[1m\u001b[35mPOST /predict HTTP/1.1\u001b[0m\" 500 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:21:09] \"\u001b[37mPOST /predict HTTP/1.1\u001b[0m\" 200 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:21:09] \"\u001b[33mGET /.js/materialize.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:21:09] \"\u001b[33mGET /js/init.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:21:10] \"\u001b[33mGET /.js/materialize.js HTTP/1.1\u001b[0m\" 404 -\n",
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"127.0.0.1 - - [27/Aug/2020 16:21:10] \"\u001b[33mGET /js/init.js HTTP/1.1\u001b[0m\" 404 -\n"
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]
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},
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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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"[10, 10, 10]\n",
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"[array([10, 10, 10])]\n"
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]
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}
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],
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"source": [
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"from flask import Flask,request, url_for, redirect, render_template\n",
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"import pickle\n",
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"import numpy as np\n",
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"\n",
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"app = Flask(__name__)\n",
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"model=pickle.load(open('model.pkl','rb'))\n",
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"\n",
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"@app.route('/')\n",
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"def hello_world():\n",
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" return render_template(\"forest_fire1.html\")\n",
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"\n",
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"@app.route('/predict',methods=['POST','GET'])\n",
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"def predict():\n",
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" # receive the values send by user in three text boxes thru request object -> requesst.form.values()\n",
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" int_features=[int(x) for x in request.form.values()]\n",
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" final=[np.array(int_features)]\n",
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" print(int_features)\n",
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" print(final)\n",
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" prediction=model.predict_proba(final)\n",
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" output='{0:.{1}f}'.format(prediction[0][1], 2)\n",
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"\n",
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" if output>str(0.5):\n",
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" return render_template('forest_fire1.html',pred='Your Forest is in Danger.\\nProbability of fire occuring is {}'.format(output))\n",
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" else:\n",
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" return render_template('forest_fire1.html',pred='Your Forest is safe.\\n Probability of fire occuring is {}'.format(output))\n",
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"\n",
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"if __name__ == '__main__':\n",
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" app.run(debug=False)\n"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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" \n",
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" \n"
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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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",
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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": "3.6.3"
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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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}

‎ClassExamples/ON47/Forest-Fire-Prediction/Forest_fire.csv‎

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Area,Oxygen,Temperature,Humidity,Fire Occurrence
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Jharkand,40,45,20,1
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Bangalore,50,30,10,1
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punjan,10,20,70,0
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chandigarh,60,45,70,1
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delhi,30,48,10,1
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Bangalore,50,15,30,0
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Jharkand,5,35,35,0
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Bangalore,5,20,70,0
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Jharkand,60,32,19,1
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Bangalore,30,50,45,1
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Jharkand,40,55,80,1
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Bangalore,30,39,100,0
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Jharkand,10,12,12,0
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Bangalore,20,23,23,0
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Ecuador,60,34,15,1
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canada,80,4,68,0
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c,25,56,79,1
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c,12,45,90,0
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asd,28,43,15,1
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asd,26,0,32,0
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ad,32,37,21,1
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sasd,45,48,12,1
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asd,56,15,65,0
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asdas,67,45,33,1
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das,78,49,8,1
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das,89,45,18,1
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ads,21,12,44,0
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ads,11,20,59,0
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asd,1,32,70,0
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,5,20,45,0
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,42,43,20,1
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,32,60,30,1
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,5,15,5,0
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,20,30,5,0
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,30,51,2,1
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,60,60,10,1
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,10,10,60,0
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,0,30,0,0
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,45,45,15,1

‎ClassExamples/ON47/Forest-Fire-Prediction/LearningFlask.ipynb‎

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{
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"cells": [
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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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"from flask import Flask\n",
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"\n",
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"#instantiate the object of Flask - app\n",
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"app = Flask(__name__)\n",
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"\n",
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"#the object app will handle incoming request from the web/browser\n",
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"# the method inside app object is route ( parameter is the path)\n",
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"# and define the method attached with that - in our example we have home()\n",
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"@app.route('/')\n",
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"def home() : \n",
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" return \"<h1> Welcome Flask </h1><br> Thank you <a href=/page2> Go to Page2 </a> \"\n",
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"\n",
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"@app.route('/page2')\n",
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"def showpage2() : \n",
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" return \"<h1> Welcome to Page2 of Website </h1><br> Deep Learning Champs \"\n",
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"\n",
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"# the default module/program name is __main__\n",
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"if __name__ == '__main__':\n",
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" app.run(debug=False)\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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n",
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"127.0.0.1 - - [27/Aug/2020 12:20:21] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n"
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]
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}
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],
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"source": [
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"#opening html page in flask\n",
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"\n",
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"from flask import Flask, render_template\n",
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"\n",
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"app = Flask(__name__)\n",
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"\n",
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"@app.route('/')\n",
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"def index():\n",
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" return render_template('first.html')\n",
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"\n",
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"if __name__ == '__main__':\n",
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" app.run()"
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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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"collapsed": true
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},
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"outputs": [],
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"source": []
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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",
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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": "3.6.3"
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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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