First Commit

README.md

@@ -1 +1,2 @@
-# Poetry-Generation-Using-Bidirectional-lstm
+# python-flask-blog
+A flask based blog with automated smtp emailer, pagination, file uploader and sql databse connected

app.py

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+from flask import Flask,render_template,request,session,redirect,jsonify
+from flask_sqlalchemy import SQLAlchemy
+import json
+from datetime import datetime
+import os
+from werkzeug.utils import secure_filename
+import math
+from flask_mail import Mail
+from model_functs import generate_text,suggestions
+from keras.models import load_model
+import re
+import os
+
+export_path = os.path.join(os.getcwd(), 'models', 'shakespeare_bi_400_ud_loss_0.66')
+shake_model=load_model(export_path)
+
+def suggest(seed,model):
+	suggested=[]
+	suggested.append(suggestions(seed,model,346).replace('\n','\\n'))
+	suggested.append(suggestions(seed,model,346//2).replace('\n','\\n'))
+	suggested.append(suggestions(seed,model,346//4).replace('\n','\\n'))
+	return suggested
+
+
+
+
+
+
+with open('config.json','r') as c:
+	params=json.load(c)['params']
+local_server=True
+
+app=Flask(__name__)
+app.secret_key='super-secret-key'
+app.config.update(
+MAIL_SERVER='smtp.gmail.com',
+MAIL_PORT='465',
+MAIL_USE_SSL='True',
+MAIL_USERNAME=params['gmail_user'],
+MAIL_PASSWORD=params['gmail_pwd']
+)
+app.config['UPLOAD_FOLDER']=params['upload_location']
+mail=Mail(app)
+
+if (local_server==True):
+	app.config['SQLALCHEMY_DATABASE_URI'] = params['local_uri']
+else:
+	app.config['SQLALCHEMY_DATABASE_URI'] = params['prod_uri']
+db=SQLAlchemy(app)
+
+class Posts(db.Model):
+	sno = db.Column(db.Integer, primary_key=True)
+	title = db.Column(db.String(50), unique=False, nullable=False)
+	slug=db.Column(db.String(21))
+	content = db.Column(db.String(120), unique=False, nullable=False)
+	date=db.Column(db.String(50))
+	img_file=db.Column(db.String(12))
+	tagline=db.Column(db.String(50))
+
+
+class Contacts(db.Model):
+    sno = db.Column(db.Integer, primary_key=True)
+    name = db.Column(db.String(50), unique=False, nullable=False)
+    phone_no=db.Column(db.String(50))
+    email = db.Column(db.String(50), unique=False, nullable=False)
+    msg=db.Column(db.String(50))
+
+@app.route('/',methods=['POST','GET'])
+def home():
+	#PAGINATION LOGIC
+	text=''
+	return render_template('index.html',params=params,text=text)
+
+@app.route('/generate_poem',methods=['POST','GET'])
+def generate_poem():
+	#PAGINATION LOGIC
+	text=''
+	if(request.method=='POST'):
+		print('successfully')
+		author=request.form.get('author')
+		len_text=request.form.get('len_text')
+		seed=request.form.get('seed')
+		print(request.form)
+		if(author=='Shakespeare'):
+			text=generate_text(seed, int(len_text), shake_model, 344)
+			text=text.split('\n')
+			if len(text[-2])//2>len(text[-1]):
+				text='\n'.join(text[:-1])
+			else:
+				text='\n'.join(text)
+	return render_template('generative.html',params=params,text=text)
+
+
+
+
+@app.route('/about')
+def about():
+	return render_template('about.html',params=params)
+
+@app.route('/contact',methods=['GET','POST'])
+def contact():
+	if (request.method=='POST'):
+		name=request.form.get('name')
+		email=request.form.get('email')
+		msg=request.form.get('msg')
+		phone=request.form.get('phone')	
+		entry=Contacts(name=name,phone_no=phone,email=email,msg=msg)
+		db.session.add(entry)
+		db.session.commit()
+		mail.send_message('Send message from '+name,
+			sender=email,recipients=[params['gmail_user']],
+			body=msg+phone)
+	return render_template('contact.html',params=params)
+
+
+@app.route('/autocomplete', methods=['POST'])
+def autocomplete():
+	if request.method=='POST':
+		results=[]
+		seed = request.form.get('seed')
+		print(seed)
+		print(type(seed))
+		results=suggest(seed, shake_model)
+		return jsonify(results)
+
+
+@app.route('/dashboard',methods=['GET','POST'])
+def dashboard():
+	if('user' in session and session['user']==params['admin_user']):
+		posts=Posts.query.all()
+		return render_template('dashboard.html',params=params,posts=posts)
+	if(request.method=='POST'):
+		u_name=request.form.get('uname')
+		pwd=request.form.get('pwd')
+		if(u_name==params['admin_user'] and pwd==params['userpass']):
+			#set the session variable
+			session['user']=u_name
+			posts=Posts.query.all()
+			return render_template('dashboard.html',params=params,posts=posts)
+	else:
+		return render_template('login.html',params=params)
+
+
+@app.route('/post/<string:post_slug>',methods=['GET'])
+def post_route(post_slug):
+	post=Posts.query.filter_by(slug=post_slug).first()
+
+	return render_template('post.html',params=params,post=post)
+@app.route('/suggestive', methods=['POST','GET'])
+def suggestive():
+	if request.method=='POST':
+		seed = request.form.get('seed')
+		print(type(seed))
+		result=suggest(seed, shake_model)
+		seed=seed.replace('\n','\\n')
+
+		return render_template('post.html',params=params,results=list(set(result)),seed_text=seed)
+	else:
+		return render_template('post.html',params=params,results=['','',''],seed_text='')
+
+
+
+if __name__ == "__main__":
+	app.run(debug=True)
+#,host='0.0.0.0',port=int(os.environ.get('PORT',8080))
+#     app.run(debug=False)

config.json

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+{
+
+	"params":
+	{
+		"local_server":"True",
+		"local_uri":"mysql://root:@localhost/flaskblog",
+		"prod_uri":"mysql://root:@localhost/flaskblog",
+		"fb_url":"https://www.facebook.com/codingthunder",
+		"blog_name":"Poetry Paradise",
+		"tag_line":"A rythemic creation of words.",
+		"gmail_user":"[email protected]",
+		"gmail_pwd":"PrekshNKV",
+		"about_text":"Our project is based on using Bi-directional LSTM (Long Short Term Memory), variant of RNN, to generate poetry in English language by feeding it a huge corpus of poems of renowned poets compiled specifically for the research. Our model has two layers with 256 units in each. We are able to analyse the overfitting nature of LSTM through our research.",
+		"no_of_posts":2,
+		"admin_user":"qwerty@q",
+		"userpass":"qwerty",
+		"upload_location":"C:\\Users\\kartikay\\Desktop\\PY files\\Flask\\static"
+	}
+}

model_functs.py

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+from keras.preprocessing.sequence import pad_sequences
+import pickle
+from tqdm import tqdm
+import re
+
+
+with open('tokenizers/shakes_tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+
+def generate_text(seed_text, next_words, model, max_sequence_len):
+    for _ in tqdm(range(next_words)):
+        token_list = tokenizer.texts_to_sequences([seed_text])[0]
+        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
+        predicted = model.predict_classes(token_list, verbose=0)
+
+        output_word = ""
+        for word,index in tokenizer.word_index.items():
+            if index == predicted:
+                output_word = word
+                break
+        seed_text += " "+output_word
+    return seed_text.title()
+
+
+def suggestions(seed_text, model, max_sequence_len):
+    generated=''
+    while True:
+        token_list = tokenizer.texts_to_sequences([seed_text])[0]
+        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
+        predicted = model.predict_classes(token_list, verbose=0)
+
+        output_word = ""
+        for word,index in tokenizer.word_index.items():
+            if index == predicted:
+                output_word = word
+                break
+        seed_text += " "+output_word
+        generated+=' '+output_word
+        if(len(generated.split(' '))>5):
+            if re.search('[^a-zA-Z|^\s]',generated)!=None:
+                break
+
+    return generated
+
+
+
+
+

models/.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

models/Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "converter=tf.lite.TFLiteConverter.from_saved_model('shakespeare_bi_400_ud_loss_0.66/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tflite_model = converter.convert()\n",
+    "\n",
+    "# Save the model.\n",
+    "with open('model.tflite', 'wb') as f:\n",
+    "    f.write(tflite_model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirements.txt

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+Keras==2.4.3
+Flask==1.1.2
+Werkzeug==1.0.1
+Flask_SQLAlchemy==2.4.4
+Flask_Mail==0.9.1
+tqdm==4.47.0
+gunicorn==19.0.0
+tensorflow-cpu==2.1.0
+pickle5==0.0.11