akash-ch2812

## result.html
<!DOCTYPE html>
<html lang="en" dir="ltr">
  <head>
    <meta charset="utf-8">
    <title>Result</title>
  </head>
  <body>
    <h1>Prediction</h1>

    {{ result }}

## index.html
<!DOCTYPE html>
<html lang="en" dir="ltr">
  <head>
    <meta charset="utf-8">
    <title>Home page</title>
  </head>
  <body>
    <h1>Titanic survival prediction</h1>
    <form action="{% urls 'result' %}">
      {% csrf_token %}

## views.py
from django.shortcuts import render

# our home page view
def home(request):
    return render(request, 'index.html')


# custom method for generating predictions
def getPredictions(pclass, sex, age, sibsp, parch, fare, C, Q, S):
    import pickle

## urls.py
# default present
from django.contrib import admin
from django.urls import path

# add this to import our views file
from Titanic_Survial_Prediction_Web import views

urlpatterns = [
    path('admin/', admin.site.urls),


## Create_Titanic_Model.py
import pandas as pd
import numpy as np

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LogisticRegression

from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

# load dataset

## Search.py
from spacy.matcher import PhraseMatcher
from scipy import spatial

# method for searching keyword from the text
def search_for_keyword(keyword, doc_obj, nlp):
    phrase_matcher = PhraseMatcher(nlp.vocab)
    phrase_list = [nlp(keyword)]
    phrase_matcher.add("Text Extractor", None, *phrase_list)

    matched_items = phrase_matcher(doc_obj)

## Similar_Keywords.py
# convert keywords to vector
def createKeywordsVectors(keyword, nlp):
    doc = nlp(keyword)  # convert to document object

    return doc.vector


# method to find cosine similarity
def cosineSimilarity(vect1, vect2):
    # return cosine distance

## Data_Preprocessing.py
import spacy
import PyPDF2

# spacy english model (large)
nlp = spacy.load('en_core_web_lg')

# method for reading a pdf file
def readPdfFile(filename, folder_name):

    # storing path of PDF-Documents folder

## Generate_Captions.py
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
%matplotlib inline

# method for generating captions
def generate_captions(model, image, tokenizer.word_index, max_caption_length, tokenizer.index_word):

    # input is <start>
    input_text = '<start>'

## Train_Caption_Model.py
# get training data
train_data = create_trianing_data(train_image_captions, train_image_features, tokenizer, max_caption_len, vocab_length, 32)

# initialize model
model = create_model(max_caption_len, vocab_len)

steps_per_epochs = len(train_image_captions)//32

# compile model
model.compile(optimizer='adam', loss='categorical_crossentropy')
	<!DOCTYPE html>
	<html lang="en" dir="ltr">
	<head>
	<meta charset="utf-8">
	<title>Result</title>
	</head>
	<body>
	<h1>Prediction</h1>

	{{ result }}
	from django.shortcuts import render

	# our home page view
	def home(request):
	return render(request, 'index.html')


	# custom method for generating predictions
	def getPredictions(pclass, sex, age, sibsp, parch, fare, C, Q, S):
	import pickle
	# default present
	from django.contrib import admin
	from django.urls import path

	# add this to import our views file
	from Titanic_Survial_Prediction_Web import views

	urlpatterns = [
	path('admin/', admin.site.urls),
	import pandas as pd
	import numpy as np

	from sklearn.model_selection import train_test_split
	from sklearn.preprocessing import MinMaxScaler
	from sklearn.linear_model import LogisticRegression

	from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

	# load dataset
	from spacy.matcher import PhraseMatcher
	from scipy import spatial

	# method for searching keyword from the text
	def search_for_keyword(keyword, doc_obj, nlp):
	phrase_matcher = PhraseMatcher(nlp.vocab)
	phrase_list = [nlp(keyword)]
	phrase_matcher.add("Text Extractor", None, *phrase_list)

	matched_items = phrase_matcher(doc_obj)
	# convert keywords to vector
	def createKeywordsVectors(keyword, nlp):
	doc = nlp(keyword) # convert to document object

	return doc.vector


	# method to find cosine similarity
	def cosineSimilarity(vect1, vect2):
	# return cosine distance
	import spacy
	import PyPDF2

	# spacy english model (large)
	nlp = spacy.load('en_core_web_lg')

	# method for reading a pdf file
	def readPdfFile(filename, folder_name):

	# storing path of PDF-Documents folder
	import matplotlib.pyplot as plt
	import seaborn as sns
	from PIL import Image
	%matplotlib inline

	# method for generating captions
	def generate_captions(model, image, tokenizer.word_index, max_caption_length, tokenizer.index_word):

	# input is <start>
	input_text = '<start>'
	# get training data
	train_data = create_trianing_data(train_image_captions, train_image_features, tokenizer, max_caption_len, vocab_length, 32)

	# initialize model
	model = create_model(max_caption_len, vocab_len)

	steps_per_epochs = len(train_image_captions)//32

	# compile model
	model.compile(optimizer='adam', loss='categorical_crossentropy')