MySQLtextAPI

A step-by-step roadmap to create a real database for text data extracted from an API using Python and MySQL

Step 1: Install Required Packages

Ensure you have the necessary packages installed:

pip install mysql-connector-python requests

Step 2: Set Up a MySQL Database

Create a MySQL database and table for storing text data.

CREATE DATABASE IF NOT EXISTS my_text_database;
USE my_text_database;

CREATE TABLE IF NOT EXISTS text_data (
    id INT AUTO_INCREMENT PRIMARY KEY,
    content TEXT,
    timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Step 3: Write a Python Script

Create a Python script to fetch data from the API and store it in the MySQL database.

import mysql.connector
import requests

# Replace these values with your MySQL connection details
mysql_host = 'your_mysql_host'
mysql_user = 'your_mysql_user'
mysql_password = 'your_mysql_password'
mysql_database = 'my_text_database'

# Step 1: Connect to MySQL database
conn = mysql.connector.connect(
    host=mysql_host,
    user=mysql_user,
    password=mysql_password,
    database=mysql_database
)
cursor = conn.cursor()

# Step 2: Fetch data from the API
api_url = 'https://api.example.com/textdata'
response = requests.get(api_url)

if response.status_code == 200:
    data_from_api = response.json()  # Assuming the API returns JSON
else:
    print(f"Error fetching data from API. Status code: {response.status_code}")
    data_from_api = []

# Step 3: Insert data into the MySQL database
for item in data_from_api:
    cursor.execute('''
        INSERT INTO text_data (content) VALUES (%s)
    ''', (item['text'],))

conn.commit()

# Step 4: Close the connection
conn.close()

Step 4: Run the Python Script

Execute the Python script to fetch data from the API and insert it into the MySQL database.

python your_script_name.py

MySQL Host:

Replace 'your_mysql_host' with the hostname or IP address of your MySQL server. For example, if your MySQL server is running locally, you might replace it with 'localhost'. If your MySQL server is hosted elsewhere, use the corresponding host information. MySQL User:
Replace 'your_mysql_user' with the username you use to connect to your MySQL server. This is often 'root' for local development, but in a production environment, you should use a dedicated user with appropriate privileges. MySQL Password:
Replace 'your_mysql_password' with the password for the MySQL user specified. If your MySQL server doesn't require a password, you can leave this empty, but it's highly recommended to use a password for security. Here's an example with the replacements:

# Replace these values with your actual MySQL connection details
mysql_host = 'localhost'  # Replace with your MySQL host
mysql_user = 'your_username'  # Replace with your MySQL username
mysql_password = 'your_password'  # Replace with your MySQL password
mysql_database = 'my_text_database'

Ensure that the values you provide are accurate, and remember to keep your database credentials secure. If you are unsure about your MySQL connection details, you may need to check with your hosting provider or database administrator.

Building a complete API and integrating machine learning involves several components and steps. I'll provide you with a simplified example that you can build upon. Note that this example includes the use of Flask for creating a simple API and scikit-learn for text preprocessing and clustering.

Step 1: Install Required Packages:

pip install flask requests beautifulsoup4 mysql-connector-python scikit-learn

Step 2: Write a Python Script (api_and_ml.py):

from flask import Flask, jsonify
import requests
from bs4 import BeautifulSoup
import mysql.connector
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import pandas as pd

app = Flask(__name__)

# Replace these values with your actual MySQL connection details
mysql_host = 'your_mysql_host'
mysql_user = 'your_mysql_user'
mysql_password = 'your_mysql_password'
mysql_database = 'my_text_database'

@app.route('/extract_and_cluster', methods=['GET'])
def extract_and_cluster():
    # Step 1: Connect to MySQL database
    conn = mysql.connector.connect(
        host=mysql_host,
        user=mysql_user,
        password=mysql_password,
        database=mysql_database
    )
    cursor = conn.cursor()

    # Step 2: Web Scraping
    url = 'https://www.varzesh3.com/'
    response = requests.get(url)

    if response.status_code == 200:
        soup = BeautifulSoup(response.text, 'html.parser')

        # Extract text data from the website (customize based on the structure of the website)
        headlines = [headline.text.strip() for headline in soup.find_all('h3', class_='headline')]

    else:
        print(f"Error fetching data from the website. Status code: {response.status_code}")
        headlines = []

    # Step 3: Insert data into the MySQL database
    for headline in headlines:
        cursor.execute('''
            INSERT INTO text_data (content) VALUES (%s)
        ''', (headline,))

    conn.commit()

    # Step 4: Fetch data from the database
    cursor.execute('SELECT content FROM text_data')
    data_from_db = [row[0] for row in cursor.fetchall()]

    # Step 5: Text Preprocessing
    vectorizer = TfidfVectorizer(stop_words='english')
    X = vectorizer.fit_transform(data_from_db)

    # Step 6: Apply Clustering (KMeans)
    kmeans = KMeans(n_clusters=5)
    kmeans.fit(X)

    # Step 7: Dimensionality Reduction (PCA)
    pca = PCA(n_components=2)
    reduced_features = pca.fit_transform(X.toarray())
    reduced_cluster_centers = pca.transform(kmeans.cluster_centers_)

    # Step 8: Prepare and return results
    results = {'data': [], 'clusters': []}

    for i, text in enumerate(data_from_db):
        cluster_label = kmeans.labels_[i]
        results['data'].append({'text': text, 'cluster': cluster_label})

    for i, center in enumerate(reduced_cluster_centers):
        results['clusters'].append({'cluster': i, 'center': center.tolist()})

    # Step 9: Close the connection
    conn.close()

    return jsonify(results)

if __name__ == '__main__':
    app.run(debug=True)

Replace 'your_mysql_host', 'your_mysql_user', and 'your_mysql_password' with your actual MySQL database connection details.

Step 3: Run the API:

Save this code in a file named api_and_ml.py. Open a terminal or command prompt, navigate to the directory where the script is saved, and execute:

python api_and_ml.py

This will start a local Flask development server.

Step 4: Access the API:

Open your web browser and go to http://127.0.0.1:5000/extract_and_cluster. This will trigger the extraction of text data from varzesh3.com, insert it into the MySQL database, preprocess the data, and apply clustering. The API will return the results in JSON format.

Note: This is a simplified example, and in a real-world scenario, you would need to implement error handling, optimize the code, and secure your API. Additionally, you may need to adjust parameters for text preprocessing and clustering based on your specific use case.

Saeidhoseinipour/MySQLtextAPI