Building an Image Recognition API using Flask.
Step 1: Set up the project environment
1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)
Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)
Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)
Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
• An example of using curl (image 5.)
• An example using Python queries (image 6.)
https://t.me/DataScienceT
Step 1: Set up the project environment
1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)
Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)
Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)
Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
• An example of using curl (image 5.)
• An example using Python queries (image 6.)
https://t.me/DataScienceT
📩 Python Email Automation Script
https://t.me/DataScienceT
import smtplib
from email.mime.text import MIMEText
sender_email = "your_email@example.com"
recipient_email = "recipient_email@example.com"
subject = "Automated Email"
message = "This is an automated email sent using Python."
# SMTP server configuration (example: Gmail)
smtp_server = "smtp.gmail.com"
smtp_port = 587
smtp_username = "your_username"
smtp_password = "your_password"
msg = MIMEText(message)
msg["Subject"] = subject
msg["From"] = sender_email
msg["To"] = recipient_email
try:
server = smtplib.SMTP(smtp_server, smtp_port)
server.starttls()
server.login(smtp_username, smtp_password)
server.sendmail(sender_email, recipient_email, msg.as_string())
print("Email sent successfully!")
except Exception as e:
print("Error sending email:", str(e))
finally:
server.quit()
https://t.me/DataScienceT
Customize the Django Admin With Python
https://realpython.com/customize-django-admin-python/
https://t.me/CodeProgrammer
https://realpython.com/customize-django-admin-python/
https://t.me/CodeProgrammer
Follow us in threads
https://www.threads.net/@pythonsy
https://www.threads.net/@pythonsy
We launched a special bot some time ago to download all scientific, software and mathematics books The bot contains more than thirty million books, and new books are downloaded first, In addition to the possibility of downloading all articles and scientific papers for free
To request a subscription: talk to @Hussein_Sheikho
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Generative Adversarial Networks
https://realpython.com/generative-adversarial-networks
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https://realpython.com/generative-adversarial-networks
https://t.me/CodeProgrammer
Important book
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50 Algorithms Every Programmer Should Know (2023)
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https://t.me/CodeProgrammer
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1- Data Science
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5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
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This book is available now only in paid channel
Cost of Paid channel is 5$ for one time and forever
Paid channel contain important book and udemy and other courses as zip files
Welcome all
Contact @Hussein_sheikho
Training and Validation Data in PyTorch
In this tutorial, you will learn about training and validation data in PyTorch. We will also demonstrate the importance of training and validation data for machine learning models in general, with a focus on neural networks.
https://machinelearningmastery.com/training-and-validation-data-in-pytorch/
https://t.me/CodeProgrammer
In this tutorial, you will learn about training and validation data in PyTorch. We will also demonstrate the importance of training and validation data for machine learning models in general, with a focus on neural networks.
https://machinelearningmastery.com/training-and-validation-data-in-pytorch/
https://t.me/CodeProgrammer
🖥 How to automate the deletion of unnecessary files using Python
Automate the removal of unnecessary files using Python.
1. Remove duplicate files
2. Deleting temporary files
3. Organize and delete files in the Downloads folder
4. Remove old/unused apps
https://t.me/CodeProgrammer
Automate the removal of unnecessary files using Python.
1. Remove duplicate files
2. Deleting temporary files
3. Organize and delete files in the Downloads folder
4. Remove old/unused apps
https://t.me/CodeProgrammer