Advanced AI Writer Code: OpenAI, TextBlob, Hugging Face, spaCy, Flask Integration for Text Generation, Grammar Correction, Summarization, Entity Extraction, and More.
Have you noticed how AI writing assistants are popping up everywhere lately? Whether it’s ChatGPT drafting an email, Grammarly fixing your grammar, or Notion helping you write blog posts—these tools are everywhere. And they’re trending for one big reason: they make writing easier. Faster. Less painful. For businesses, it’s about scaling content. For students, it’s about clarity. For creators, it’s about pushing past the blank page. And in this post, I’ll show you how to build your own advanced AI writer—from scratch to smart.
Before starting into the construction of an advanced AI writer, it’s important to understand the basics of fundamentals of how AI writing works. AI writers are built using natural language processing (NLP) and machine learning (ML) technologies. NLP allows machines to understand and produce human language, while ML allows the system to learn and improve from data.
Begin by identifying the specific requirements and goals for your AI writer. Consider the following questions:
Data is the backbone of any AI project. For an AI writer, you’ll need a substantial amount of text data to train your model. Sources can include:
Selecting the appropriate framework and tools is crucial. Popular frameworks for building AI writers include:
Data preprocessing involves cleaning and preparing your text data for training. This includes:
With your data prepared, it’s time to train your model. This involves:
After training, evaluate your model’s performance using metrics like perplexity (for language models) or BLEU score (for translation tasks). Optimize your model by:
Once your model meets the desired performance criteria, deploy it into a production environment. Consider using cloud services like AWS, Google Cloud, or Azure for scalability and reliability.
AI models require ongoing maintenance to ensure they continue to perform well. Monitor your AI writer’s output for quality and relevance, and update the model as needed with new data or improved algorithms.
Let’s start a step-by-step approach to developing an advanced AI writer application, covering everything from environment setup and library imports to deployment considerations. Each step is explained in detail to ensure you have a clear understanding of the process.
To begin, make sure you have all the necessary packages installed. These packages are important for various tasks such as natural language processing (NLP), machine learning, web framework functionalities, and more.
pip install openai transformers spacy textblob googletrans==4.0.0-rc1 flask flask-httpauth gunicorn
To build a powerful and secure AI writing application, you can use several key tools and libraries:
Combining these tools enables the creation of an advanced AI writing tool with a web interface and efficient handling of production-level traffic.
In this step, you’ll import all the necessary libraries which you installed before, to build your AI writer. These libraries provide the tools and functionalities required for various natural language processing tasks.
import openai
import spacy
from textblob import TextBlob
from transformers import pipeline
from googletrans import Translator
from flask import Flask, request, jsonify, abort
from flask_httpauth import HTTPBasicAuth
from werkzeug.security import generate_password_hash, check_password_hash
# Load Spacy model for NLP tasks
nlp = spacy.load('en_core_web_sm')
# Initialize the translator
translator = Translator()
# Initialize Flask and HTTPBasicAuth
app = Flask(__name__)
auth = HTTPBasicAuth()
To interact with OpenAI’s language models, you need to configure the OpenAI API by setting your API key. This key is a unique identifier that allows your application to authenticate and communicate with OpenAI’s servers.
In your Python script, you need to set the openai.api_key variable to your API key. This configuration step allows your script to authenticate with OpenAI’s servers.Replace 'your-api-key' with your actual API key.
openai.api_key = 'your-api-key'
Never Hardcode API Keys in Public Repositories: If you’re using version control systems like Git, make sure your API keys are not hardcoded in the files that you push to public repositories. Instead, use environment variables or configuration files that are not included in your public codebase.
import openai
import os
# Set the API key from an environment variable
openai.api_key = os.getenv('OPENAI_API_KEY')
In this example, you would set the script to read the OPENAI_API_KEY environment variable set in your operating system. This approach strengthens security by ensuring sensitive information remains separate from your codebase.
You have to define various functions to handle different features of your AI writer, including error handling to manage exceptions gracefully.
def generate_text(prompt, model="text-davinci-003", max_tokens=150):
try:
response = openai.Completion.create(
engine=model,
prompt=prompt,
max_tokens=max_tokens
)
return response.choices[0].text.strip()
except Exception as e:
return str(e)
The generate_text function uses OpenAI’s API to create text based on a given input prompt. Here’s a summary:
“This function is defined with three parameters:
prompt: This is the text or prompt used by the AI model for text generation.model: It specifies the AI model to be used; by default, it is set to “text-davinci-003”.max_tokens: This parameter determines the maximum number of words or characters in the generated text, thus controlling its length.Within the function, it utilizes openai.Completion.create to generate text through the OpenAI API. This method employs the provided model, prompt, and max_tokens to generate text.
The function is enclosed in a try-except block to handle potential errors, like network issues or API limits. Within this block:
openai.Completion.create to generate text via the OpenAI API.Exception Handling: In case of an exception during the API call (e.g., network errors), the function captures it and returns an error message.
Return Value: The function returns the generated text if successful; otherwise, it returns an error message string.
Basically, generate_text offers a streamlined method to employ OpenAI’s robust text generation capabilities, ensuring smooth operation while handling potential errors.
def check_grammar(text):
try:
blob = TextBlob(text)
corrected_text = blob.correct()
return str(corrected_text)
except Exception as e:
return str(e)
The check_grammar function uses TextBlob, a natural language processing library, to rectify grammatical errors within the provided text. Here’s a summary:
The check_grammar function accepts one parameter:
text: The input text requiring scrutiny for grammatical errors.
Enclosed within a try-except block, check_grammar assures strong error handling to manage potential exceptions during execution, preventing script crashes.
correct() method of the TextBlob object is invoked to rectify any identified grammatical errors within the input text.In general, check_grammar provides a simple means to authorize TextBlob’s grammar correction capabilities, enhancing the accuracy and clarity of input text by addressing grammatical inconsistencies.
summarizer = pipeline("summarization")
def summarize_text(text, max_length=50):
try:
summary = summarizer(text, max_length=max_length, min_length=25, do_sample=False)
return summary[0]['summary_text']
except Exception as e:
return str(e)
This Python script defines a function for text summarization using Hugging Face’s summarization pipeline.
pipeline function from the Hugging Face library.summarize_text function takes two parameters: text: The input text to be summarized.max_length: The maximum length of the summary (default is 50 words).summarizer pipeline with specified parameters like max_length and min_length.In summary, this script provides a simple and convenient way to generate text summaries using pre-trained models provided by Hugging Face.
def extract_entities(text):
try:
doc = nlp(text)
entities = [(entity.text, entity.label_) for entity in doc.ents]
return entities
except Exception as e:
return str(e)
This Python script defines a function called extract_entities for context understanding through Named Entity Recognition (NER). Here’s a brief explanation:
text, which represents the input text containing entities.nlp). This model is assumed to have been initialized elsewhere in the code.doc) and stores each entity’s text and label (type) in a tuple.In summary, this script provides a method to extract entities such as names of people, organizations, locations, etc., from a given input text using a Named Entity Recognition model.
def adjust_style(text, style="formal"):
try:
prompt = f"Rewrite the following text in a {style} style:\n\n{text}"
return generate_text(prompt)
except Exception as e:
return str(e)
adjust_style takes two parameters: text: The input text whose style needs adjustment.style: The desired style for the output text (default is “formal”).generate_text) to generate text using OpenAI’s API, passing the constructed prompt as input.In summary, this script provides a convenient method to adjust the writing style of input text by utilizing OpenAI’s API for text generation.
def analyze_sentiment(text):
try:
blob = TextBlob(text)
return blob.sentiment
except Exception as e:
return str(e)
The function analyze_sentiment takes a single parameter:
text: The input text whose sentiment needs to be analyzed.To sum up, this script provides a direct approach to assess the sentiment of given text using the TextBlob library, delivering both polarity and subjectivity scores as results.
def translate_text(text, dest_language='en'):
try:
translation = translator.translate(text, dest=dest_language)
return translation.text
except Exception as e:
return str(e)
The function translate_text accepts two parameters:
text: The input text to be translated.dest_language: The desired language into which the text will be translated (default is English, abbreviated as ‘en’).translator.translate method, passing the input text and the destination language as arguments.
import requests
def custom_model_integration(prompt, api_url):
try:
response = requests.post(api_url, json={'prompt': prompt})
return response.json().get('text', 'Error: No text returned from custom model')
except Exception as e:
return str(e)
This Python script defines a function called custom_model_integration that facilitates integration with a custom model API to generate text based on a provided prompt. Here’s a brief explanation:
The custom_model_integration function accepts two parameters:
prompt: The text prompt used for generating the desired text.api_url: The URL endpoint of the custom model API.The function initiates a POST request to the designated API URL, passing the prompt as JSON data in the request body.
Subsequently, it endeavors to extract the generated text from the API response received.
In case of success, it returns the extracted generated text.
However, if any exception arises during the operation, such as network issues or invalid responses, the function returns an error message as a string.
Create an AIWriter class that integrates all the defined functions and handles the OpenAI API key configuration.
class AIWriter:
def __init__(self, api_key):
openai.api_key = api_key
def generate(self, prompt, max_tokens=150):
return generate_text(prompt, max_tokens=max_tokens)
def check_grammar(self, text):
return check_grammar(text)
def summarize(self, text, max_length=50):
return summarize_text(text, max_length=max_length)
def extract_entities(self, text):
return extract_entities(text)
def adjust_style(self, text, style="formal"):
return adjust_style(text, style)
def analyze_sentiment(self, text):
return analyze_sentiment(text)
def translate(self, text, dest_language='en'):
return translate_text(text, dest_language)
def custom_integration(self, prompt, api_url):
return custom_model_integration(prompt, api_url)
AIWriter: A class that integrates all AI writing functionalities.
Set up basic authentication to secure the API endpoints.
This Python code snippet manages user authentication using hashed passwords.
users = {
"admin": generate_password_hash("secret"),
"user": generate_password_hash("password")
}
@auth.verify_password
def verify_password(username, password):
if username in users and check_password_hash(users.get(username), password):
return username
Develop the Flask API to handle various requests and ensure proper error handling and authentication.
@app.errorhandler(400)
def bad_request(error):
return jsonify({'error': 'Bad Request', 'message': str(error)}), 400
@app.errorhandler(401)
def unauthorized(error):
return jsonify({'error': 'Unauthorized', 'message': str(error)}), 401
@app.errorhandler(404)
def not_found(error):
return jsonify({'error': 'Not Found', 'message': str(error)}), 404
@app.errorhandler(500)
def internal_error(error):
return jsonify({'error': 'Internal Server Error', 'message': str(error)}), 500
@app.route('/generate', methods=['POST'])
@auth.login_required
def generate():
data = request.json
prompt = data.get('prompt')
max_tokens = data.get('max_tokens', 150)
if not prompt:
abort(400, description="Prompt is required")
result = writer.generate(prompt, max_tokens)
return jsonify({'text': result})
@app.route('/check_grammar', methods=['POST'])
@auth.login_required
def check_grammar():
data = request.json
text = data.get('text')
if not text:
abort(400, description="Text is required")
result = writer.check_grammar(text)
return jsonify({'corrected_text': result})
@app.route('/summarize', methods=['POST'])
@auth.login_required
def summarize():
data = request.json
text = data.get('text')
max_length = data.get('max_length', 50)
if not text:
abort(400, description="Text is required")
result = writer.summarize(text, max_length)
return jsonify({'summary': result})
@app.route('/extract_entities', methods=['POST'])
@auth.login_required
def extract_entities():
data = request.json
text = data.get('text')
if not text:
abort(400, description="Text is required")
result = writer.extract_entities(text)
return jsonify({'entities': result})
@app.route('/adjust_style', methods=['POST'])
@auth.login_required
def adjust_style():
data = request.json
text = data.get('text')
style = data.get('style', 'formal')
if not text:
abort(400, description="Text is required")
result = writer.adjust_style(text, style)
return jsonify({'styled_text': result})
@app.route('/analyze_sentiment', methods=['POST'])
@auth.login_required
def analyze_sentiment():
data = request.json
text = data.get('text')
if not text:
abort(400, description="Text is required")
result = writer.analyze_sentiment(text)
return jsonify({'sentiment': result._asdict()})
@app.route('/translate', methods=['POST'])
@auth.login_required
def translate():
data = request.json
text = data.get('text')
dest_language = data.get('dest_language', 'en')
if not text:
abort(400, description="Text is required")
result = writer.translate(text, dest_language)
return jsonify({'translated_text': result})
if __name__ == '__main__':
app.run(debug=True)
Deploy the Flask app using Gunicorn for production readiness.
gunicorn_config.py)
bind = "0.0.0.0:8000"
workers = 4
gunicorn -c gunicorn_config.py app:app
Gunicorn Command: Starts the Flask app using Gunicorn with the specified configuration.
This comprehensive guide gives you detailed instructions for setting up the environment, importing necessary libraries, configuring the OpenAI API, defining various AI writer functions, integrating these functions into a class, setting up authentication, creating a Flask API, and deploying the app with Gunicorn. This setup ensures a secure, strong, and production-ready AI writer application that can be accessed through RESTful API endpoints. For further security, consider using HTTPS and more sophisticated authentication mechanisms like OAuth.
To ensure your AI writer application is robust and secure, consider the following additional steps and improvements:
To enhance the security of your application, especially if it’s exposed to the public internet, you should implement HTTPS and consider more advanced authentication mechanisms.
Use Let’s Encrypt:
Proxy Setup:
server {
listen 80;
server_name your_domain.com;
return 301 https://$host$request_uri;
}
server {
listen 443 ssl;
server_name your_domain.com;
ssl_certificate /etc/letsencrypt/live/your_domain.com/fullchain.pem;
ssl_certificate_key /etc/letsencrypt/live/your_domain.com/privkey.pem;
location / {
proxy_pass http://127.0.0.1:8000;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
}
}
OAuth2:
authlib or flask-oauthlib to integrate OAuth2 with your Flask application.
from authlib.integrations.flask_client import OAuth
oauth = OAuth(app)
oauth.register(
name='example',
client_id='YOUR_CLIENT_ID',
client_secret='YOUR_CLIENT_SECRET',
authorize_url='https://provider.com/oauth/authorize',
authorize_params=None,
access_token_url='https://provider.com/oauth/token',
access_token_params=None,
refresh_token_url=None,
redirect_uri='https://your_domain.com/auth/callback',
client_kwargs={'scope': 'openid profile email'}
)
@app.route('/login')
def login():
redirect_uri = url_for('auth', _external=True)
return oauth.example.authorize_redirect(redirect_uri)
@app.route('/auth/callback')
def auth_callback():
token = oauth.example.authorize_access_token()
user = oauth.example.parse_id_token(token)
return jsonify(user)
Implement monitoring and logging to keep track of your application’s performance and errors.
logging module to log important events and errors.
import logging
from logging.handlers import RotatingFileHandler
handler = RotatingFileHandler('app.log', maxBytes=10000, backupCount=3)
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]')
handler.setFormatter(formatter)
app.logger.addHandler(handler)
Export Flask metrics to Prometheus using prometheus_flask_exporter.
from prometheus_flask_exporter import PrometheusMetrics
metrics = PrometheusMetrics(app)
Consider how your application can scale to handle increased load.
a. Load Balancing:
b. Auto-Scaling:
Implement CI/CD pipelines to automate testing, deployment, and updates.
a. CI/CD Tools:
Start Small: Begin with a manageable project scope and gradually expand as you gain more experience.
Leverage Community Resources: Utilize forums, GitHub repositories, and online courses to stay updated with the latest advancements.
Ensure Ethical Use: Be mindful of ethical considerations, such as data privacy and the potential for bias in AI-generated content.
Building your own advanced AI writer is a challenging but rewarding endeavor. By following this guide, you can create a powerful tool that automates content creation and enhances productivity. Remember to stay curious, experiment with different approaches, and continuously learn from the AI community. With dedication and persistence, you’ll be well on your way to mastering the art of AI writing.
ai_writer_project/
│
├── app/
│ ├── __init__.py
│ ├── routes.py
│ ├── utils.py
│ ├── auth.py
│ └── ai_writer.py
│
├── gunicorn_config.py
├── requirements.txt
└── run.py
from flask import Flask
from flask_httpauth import HTTPBasicAuth
app = Flask(__name__)
auth = HTTPBasicAuth()
from app import routes
from flask import request, jsonify, abort
from app import app, auth
from app.utils import verify_password
from app.ai_writer import AIWriter
# Initialize AI Writer
api_key = 'your-openai-api-key'
writer = AIWriter(api_key)
@app.errorhandler(400)
def bad_request(error):
return jsonify({'error': 'Bad Request', 'message': str(error)}), 400
@app.errorhandler(401)
def unauthorized(error):
return jsonify({'error': 'Unauthorized', 'message': str(error)}), 401
@app.errorhandler(404)
def not_found(error):
return jsonify({'error': 'Not Found', 'message': str(error)}), 404
@app.errorhandler(500)
def internal_error(error):
return jsonify({'error': 'Internal Server Error', 'message': str(error)}), 500
@app.route('/generate', methods=['POST'])
@auth.login_required
def generate():
data = request.json
prompt = data.get('prompt')
max_tokens = data.get('max_tokens', 150)
if not prompt:
abort(400, description="Prompt is required")
result = writer.generate(prompt, max_tokens)
return jsonify({'text': result})
@app.route('/check_grammar', methods=['POST'])
@auth.login_required
def check_grammar():
data = request.json
text = data.get('text')
if not text:
abort(400, description="Text is required")
result = writer.check_grammar(text)
return jsonify({'corrected_text': result})
@app.route('/summarize', methods=['POST'])
@auth.login_required
def summarize():
data = request.json
text = data.get('text')
max_length = data.get('max_length', 50)
if not text:
abort(400, description="Text is required")
result = writer.summarize(text, max_length)
return jsonify({'summary': result})
@app.route('/extract_entities', methods=['POST'])
@auth.login_required
def extract_entities():
data = request.json
text = data.get('text')
if not text:
abort(400, description="Text is required")
result = writer.extract_entities(text)
return jsonify({'entities': result})
@app.route('/adjust_style', methods=['POST'])
@auth.login_required
def adjust_style():
data = request.json
text = data.get('text')
style = data.get('style', 'formal')
if not text:
abort(400, description="Text is required")
result = writer.adjust_style(text, style)
return jsonify({'styled_text': result})
@app.route('/analyze_sentiment', methods=['POST'])
@auth.login_required
def analyze_sentiment():
data = request.json
text = data.get('text')
if not text:
abort(400, description="Text is required")
result = writer.analyze_sentiment(text)
return jsonify({'sentiment': result._asdict()})
@app.route('/translate', methods=['POST'])
@auth.login_required
def translate():
data = request.json
text = data.get('text')
dest_language = data.get('dest_language', 'en')
if not text:
abort(400, description="Text is required")
result = writer.translate(text, dest_language)
return jsonify({'translated_text': result})
from app import auth
from werkzeug.security import generate_password_hash, check_password_hash
# Define Users
users = {
"admin": generate_password_hash("secret"),
"user": generate_password_hash("password")
}
@auth.verify_password
def verify_password(username, password):
if username in users and check_password_hash(users.get(username), password):
return username
from app import auth
@auth.error_handler
def auth_error():
return jsonify({'error': 'Unauthorized access'}), 401
import openai
import spacy
from textblob import TextBlob
from transformers import pipeline
from googletrans import Translator
import requests
# Load Spacy model for NLP tasks
nlp = spacy.load('en_core_web_sm')
# Initialize the translator
translator = Translator()
class AIWriter:
def __init__(self, api_key):
openai.api_key = api_key
def generate(self, prompt, max_tokens=150):
try:
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
max_tokens=max_tokens
)
return response.choices[0].text.strip()
except Exception as e:
return str(e)
def check_grammar(self, text):
try:
blob = TextBlob(text)
corrected_text = blob.correct()
return str(corrected_text)
except Exception as e:
return str(e)
def summarize(self, text, max_length=50):
summarizer = pipeline("summarization")
try:
summary = summarizer(text, max_length=max_length, min_length=25, do_sample=False)
return summary[0]['summary_text']
except Exception as e:
return str(e)
def extract_entities(self, text):
try:
doc = nlp(text)
entities = [(entity.text, entity.label_) for entity in doc.ents]
return entities
except Exception as e:
return str(e)
def adjust_style(self, text, style="formal"):
try:
prompt = f"Rewrite the following text in a {style} style:\n\n{text}"
return self.generate(prompt)
except Exception as e:
return str(e)
def analyze_sentiment(self, text):
try:
blob = TextBlob(text)
return blob.sentiment
except Exception as e:
return str(e)
def translate(self, text, dest_language='en'):
try:
translation = translator.translate(text, dest=dest_language)
return translation.text
except Exception as e:
return str(e)
def custom_integration(self, prompt, api_url):
try:
response = requests.post(api_url, json={'prompt': prompt})
return response.json().get('text', 'Error: No text returned from custom model')
except Exception as e:
return str(e)
bind = "0.0.0.0:8000"
workers = 4
flask
flask-httpauth
gunicorn
openai
transformers
spacy
textblob
googletrans==4.0.0-rc1
requests
from app import app
if __name__ == '__main__':
app.run(debug=True)
This project structure includes all necessary files and directories to create, run, and deploy the AI Writer Flask application.
Here are key resources for building and training AI models, with a focus on natural language processing (NLP):
After debugging production systems that process millions of records daily and optimizing research pipelines that…
The landscape of Business Intelligence (BI) is undergoing a fundamental transformation, moving beyond its historical…
The convergence of artificial intelligence and robotics marks a turning point in human history. Machines…
The journey from simple perceptrons to systems that generate images and write code took 70…
In 1973, the British government asked physicist James Lighthill to review progress in artificial intelligence…
Expert systems came before neural networks. They worked by storing knowledge from human experts as…
This website uses cookies.