Learn With Frahim

Day 10: Project Day – Automation + Cloud Mini Project

Step 1: Project Overview

We will build a **Weather Automation Project**:

Fetch real-time weather data from OpenWeatherMap API
Store it locally in SQLite for backup
Upload to Cloud SQL on GCP
Analyze and generate daily reports (CSV & plots)
Optional: Send automated email with report

Step 2: Project Folder Structure

Create a folder like:

C:/PythonTutorial/Day10_Project/
│
├── data/
│   └── weather_backup.db
├── reports/
│   └── daily_report.csv
├── main.py
├── config.py
└── requirements.txt

Explanation: data/ stores local DB, reports/ stores CSV output, main.py is main script, config.py stores API keys and DB credentials.

Step 3: Install Dependencies

pip install requests pandas sqlalchemy pymysql matplotlib seaborn

Explanation: requests for API calls, pandas for data handling, sqlalchemy+pymysql for cloud DB, matplotlib & seaborn for plots.

Step 4: Configuration File (config.py)

API_KEY = "YOUR_OPENWEATHER_API_KEY"  # API key

CITY = "Dallas"

SQL_USER = "your_user"

SQL_PASSWORD = "your_password"

SQL_HOST = "YOUR_PUBLIC_IP"

SQL_DB = "weatherdb"

Step 5: Main Automation Script (main.py)

import requests

import sqlite3

import pandas as pd

from sqlalchemy import create_engine

import matplotlib.pyplot as plt

import seaborn as sns

from config import API_KEY, CITY, SQL_USER, SQL_PASSWORD, SQL_HOST, SQL_DB

# 1️⃣ Fetch weather data from API

url = f"http://api.openweathermap.org/data/2.5/weather?q={CITY}&appid={API_KEY}&units=metric"

response = requests.get(url)

data = response.json()

temperature = data['main']['temp']

weather_desc = data['weather'][0]['description']

print(f"Weather in {CITY}: {temperature}°C, {weather_desc}")

# 2️⃣ Store locally in SQLite

conn = sqlite3.connect("data/weather_backup.db")

c = conn.cursor()

c.execute('''CREATE TABLE IF NOT EXISTS Weather
             (City TEXT, Temperature REAL, Description TEXT)''')

c.execute("INSERT INTO Weather VALUES (?,?,?)", (CITY, temperature, weather_desc))

conn.commit()

conn.close()

# 3️⃣ Upload to Cloud SQL (MySQL)

engine = create_engine(f'mysql+pymysql://{SQL_USER}:{SQL_PASSWORD}@{SQL_HOST}/{SQL_DB}')

df = pd.DataFrame({'City':[CITY], 'Temperature':[temperature], 'Description':[weather_desc]})

df.to_sql('Weather', con=engine, if_exists='append', index=False)

# 4️⃣ Read cloud data and generate daily report

df_cloud = pd.read_sql("SELECT * FROM Weather", con=engine)

df_cloud.to_csv("reports/daily_report.csv", index=False)

# 5️⃣ Plot temperature trend

sns.set(style="whitegrid")

plt.figure(figsize=(8,5))

sns.lineplot(data=df_cloud, x=df_cloud.index, y="Temperature", marker="o")

plt.title("Temperature Trend")

plt.xlabel("Record Number")

plt.ylabel("Temperature (°C)")

plt.savefig("reports/temperature_trend.png")  # Save plot

plt.show()

Explanation: The script performs API fetch, local backup, cloud upload, report generation, and plotting in one automated workflow.

Step 6: Run Project

Run the script in command line or IDE:

python main.py

Expected output:

Print weather data on console
Store in data/weather_backup.db
Upload to cloud table Weather
Create reports/daily_report.csv and reports/temperature_trend.png

Step 7: Optional – Email Automation

Send daily report automatically using smtplib:

import smtplib

from email.mime.text import MIMEText

from email.mime.multipart import MIMEMultipart

sender = "you@gmail.com"

receiver = "recipient@gmail.com"

subject = "Daily Weather Report"

body = "Please find attached the daily weather report."

msg = MIMEMultipart()

msg['From'] = sender

msg['To'] = receiver

msg['Subject'] = subject

msg.attach(MIMEText(body, 'plain'))

# Attach CSV

with open("reports/daily_report.csv", "r") as f:

    attachment = MIMEText(f.read())

    attachment.add_header('Content-Disposition', 'attachment', filename="daily_report.csv")

    msg.attach(attachment)

# Send email

server = smtplib.SMTP('smtp.gmail.com', 587)

server.starttls()

server.login(sender, "YOUR_APP_PASSWORD")

server.send_message(msg)

server.quit()

Explanation: Uses Gmail SMTP to send report automatically. For Gmail, generate an App Password for security.

Step 8: Project Cheat Sheet

API fetch: requests.get(url), response.json()
SQLite: sqlite3.connect(), cursor.execute(), commit()
Cloud SQL: SQLAlchemy engine, df.to_sql(), pd.read_sql()
Pandas: df.to_csv(), df.head(), df.describe()
Visualization: seaborn.lineplot(), plt.savefig(), plt.show()
Email: smtplib.SMTP(), MIMEMultipart(), MIMEText(), server.send_message()

✔ End of Day 10 – Project complete! You now have an automated Python workflow fetching APIs, storing locally and in cloud, analyzing data, plotting, and optionally emailing daily reports.

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