bharat choudhary bharatc9530

## AboutMe.gif

      
              2 files
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                bharatc9530
                / AboutMe.gif
            
            
              Last active
              August 21, 2021 18:28
            
              
                About Me 😎
              
          
## iris.py
import pandas as pd
import numpy as np
import pickle

df = pd.read_csv('iris.data')

X = np.array(df.iloc[:, 0:4])
y = np.array(df.iloc[:, 4:])

from sklearn.preprocessing import LabelEncoder

## app.py
from flask import Flask, render_template, request
import pickle
import numpy as np


app = Flask(__name__)
model = pickle.load(open('iri.pkl', 'rb'))


@app.route('/')

## home.html
<html>
    <body bgcolor=#d4a3ae>

        <center>

        <h1> IRIS FLOWER DETECTION </h1><br>

        <form method="POST", action="{{url_for('home')}}">
           <b> First value :  <input type="text", name='a', placeholder="enter 1"> <br><br>
            Second value :  <input type="text", name='b', placeholder="enter 2"> <br><br>

## about.html
<html>

<body bgcolor=#9d3bc4>

    <center>

        <h1>  PREDICTION :  </h1>

    {%if data == 0%}
    <h1>Iris-setosa</h1>

## hemorrhage.png

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                bharatc9530
                / hemorrhage.png
            
            
              Last active
              August 3, 2020 08:37
            
              
                Intracranial hemorrhage (ICH) 
              
          
## first.py
import pandas as pd
import numpy as np
from numpy import array
from keras.preprocessing.text import one_hot, Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.embeddings import Embedding

## load.py
df = pd.read_csv('Review.csv').sample(frac=1).reset_index(drop=True)
df['sentiment'] = df['sentiment'].astype('category',inplace = True).cat.codes
df.head()

## split.py
docs = df['review']
labels = array(df['sentiment'])
from sklearn.model_selection import train_test_split
X_train, X_test , y_train, y_test = train_test_split(docs, labels , test_size = 0.40)

## one_hot.py
t = Tokenizer()
t.fit_on_texts(docs)
vocab_size = len(t.word_index) + 1
# integer encode the documents
print(vocab_size)

X_train = [one_hot(d, vocab_size,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~',lower=True, split=' ') for d in X_train]
X_test = [one_hot(d, vocab_size,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{|}~',lower=True, split=' ') for d in X_test]
	import pandas as pd
	import numpy as np
	import pickle

	df = pd.read_csv('iris.data')

	X = np.array(df.iloc[:, 0:4])
	y = np.array(df.iloc[:, 4:])

	from sklearn.preprocessing import LabelEncoder
	from flask import Flask, render_template, request
	import pickle
	import numpy as np


	app = Flask(__name__)
	model = pickle.load(open('iri.pkl', 'rb'))


	@app.route('/')
	<html>
	<body bgcolor=#d4a3ae>

	<center>

	<h1> IRIS FLOWER DETECTION </h1><br>

	<form method="POST", action="{{url_for('home')}}">
	<b> First value : <input type="text", name='a', placeholder="enter 1"> <br><br>
	Second value : <input type="text", name='b', placeholder="enter 2"> <br><br>
	<html>

	<body bgcolor=#9d3bc4>

	<center>

	<h1> PREDICTION : </h1>

	{%if data == 0%}
	<h1>Iris-setosa</h1>
	import pandas as pd
	import numpy as np
	from numpy import array
	from keras.preprocessing.text import one_hot, Tokenizer
	from keras.preprocessing.sequence import pad_sequences
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.layers import Flatten
	from keras.layers.embeddings import Embedding
	df = pd.read_csv('Review.csv').sample(frac=1).reset_index(drop=True)
	df['sentiment'] = df['sentiment'].astype('category',inplace = True).cat.codes
	df.head()
	docs = df['review']
	labels = array(df['sentiment'])
	from sklearn.model_selection import train_test_split
	X_train, X_test , y_train, y_test = train_test_split(docs, labels , test_size = 0.40)
	t = Tokenizer()
	t.fit_on_texts(docs)
	vocab_size = len(t.word_index) + 1
	# integer encode the documents
	print(vocab_size)

	X_train = [one_hot(d, vocab_size,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{\|}~',lower=True, split=' ') for d in X_train]
	X_test = [one_hot(d, vocab_size,filters='!"#$%&()*+,-./:;<=>?@[\]^_`{\|}~',lower=True, split=' ') for d in X_test]