def add_item_to_sale(self):
    barcode = self.barcode_input.text()
    if not barcode:
        return
    
    drug = db.find_drug_by_barcode(barcode)
    
    if not drug:
        QMessageBox.warning(self, "Not Found", "No drug found with this barcode.")
        self.barcode_input.clear()
        return
    
    drug_id = drug[0]
    
    if drug[3] <= 0: # Check quantity
         QMessageBox.warning(self, "Out of Stock", f"{drug[1]} is out of stock.")
         self.barcode_input.clear()
         return

    if drug_id in self.current_sale_items:
        # Item already in sale, increment quantity
        self.current_sale_items[drug_id]['quantity'] += 1
    else:
        # Add new item to sale
        self.current_sale_items[drug_id] = {
            'data': drug,
            'quantity': 1
        }
    
    self.update_sales_table()
    self.barcode_input.clear()

def update_sales_table(self):
    self.sales_table.setRowCount(len(self.current_sale_items))
    total_sale_amount = 0.0
    
    for row, item in enumerate(self.current_sale_items.values()):
        drug_data = item['data']
        quantity = item['quantity']
        unit_price = drug_data[4]
        total_price = quantity * unit_price
        
        self.sales_table.setItem(row, 0, QTableWidgetItem(str(drug_data[0]))) # ID
        self.sales_table.setItem(row, 1, QTableWidgetItem(drug_data[1]))      # Name
        self.sales_table.setItem(row, 2, QTableWidgetItem(str(quantity)))
        self.sales_table.setItem(row, 3, QTableWidgetItem(f"{unit_price:.2f}"))
        self.sales_table.setItem(row, 4, QTableWidgetItem(f"{total_price:.2f}"))
        
        total_sale_amount += total_price
        
    self.total_amount_label.setText(f"{total_sale_amount:.2f}")

def complete_sale(self):
    if not self.current_sale_items:
        return

    for drug_id, item in self.current_sale_items.items():
        db.update_drug_quantity(drug_id, item['quantity'])
    
    QMessageBox.information(self, "Success", f"Sale completed. Total: {self.total_amount_label.text()}")
    
    self.clear_sale()
    self.load_inventory_data() # Refresh inventory tab to show new quantities

def clear_sale(self):
    self.current_sale_items.clear()
    self.update_sales_table()

#Hashtags: #BusinessLogic #PointOfSale #PythonCode #Transaction

---

#Step 5: Results and Discussion

With all the code in place, you have a fully functional pharmacy management system.

How to Use It:
• Run the main.py noscript.
• Go to the "Inventory Management" tab and add a few drugs with unique barcodes.
• Go to the "Point of Sale" tab. The cursor will be in the barcode input field.
• Type a barcode of a drug you added and press Enter. The drug will appear in the sales table.
• Scan the same barcode again. The quantity for that drug in the sales table will increase to 2.
• Click "Complete Sale". A success message will appear. The sales table will clear.
• Switch back to the "Inventory Management" tab. You will see that the quantity of the sold drugs has decreased accordingly.

Discussion and Potential Improvements:
Real Barcode Scanner: This application works directly with a USB barcode scanner. A scanner acts as a keyboard, so when it scans a code, it types the numbers and sends an "Enter" keystroke, which perfectly triggers our returnPressed signal.
Data Integrity: We added a basic check for stock (quantity > 0). A more robust system would check if the quantity in the cart exceeds the quantity in stock before allowing the sale to complete.
Features for a Real Pharmacy: A production-level system would need many more features: prenoscription management, patient records, batch tracking for recalls, advanced reporting (e.g., top-selling drugs, low-stock alerts), user accounts with different permission levels, and receipt printing.
Database: SQLite is perfect for a single-user, standalone application. For a pharmacy with multiple terminals, a client-server database like PostgreSQL or MySQL would be necessary.

This project provides a solid foundation, demonstrating how to integrate hardware (like a barcode scanner) with a database-backed desktop application to solve a real-world business problem.

#ProjectComplete #SoftwareEngineering #PythonGUI #HealthTech

━━━━━━━━━━━━━━━
By: @DataScienceN ✨

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💡 Top 70 Web Scraping Operations in Python

I. Making HTTP Requests (requests)

• Import the library.

import requests

• Make a GET request to a URL.

response = requests.get('http://example.com')

• Check the response status code (200 is OK).

print(response.status_code)

• Access the raw HTML content (as bytes).

html_bytes = response.content

• Access the HTML content (as a string).

html_text = response.text

• Access response headers.

print(response.headers)

• Send a custom User-Agent header.

headers = {'User-Agent': 'My Cool Scraper 1.0'}
response = requests.get('http://example.com', headers=headers)

• Pass URL parameters in a request.

params = {'q': 'python scraping'}
response = requests.get('https://www.google.com/search', params=params)

• Make a POST request with form data.

payload = {'key1': 'value1', 'key2': 'value2'}
response = requests.post('http://httpbin.org/post', data=payload)

• Handle potential request errors.

try:
    response = requests.get('http://example.com', timeout=5)
    response.raise_for_status() # Raise an exception for bad status codes
except requests.exceptions.RequestException as e:
    print(f"An error occurred: {e}")

II. Parsing HTML with BeautifulSoup (Setup & Navigation)

• Import the library.

from bs4 import BeautifulSoup

• Create a BeautifulSoup object from HTML text.

soup = BeautifulSoup(html_text, 'html.parser')

• Prettify the parsed HTML for readability.

print(soup.prettify())

• Access a tag directly by name (gets the first one).

noscript_tag = soup.noscript

• Navigate to a tag's parent.

noscript_parent = soup.noscript.parent

• Get an iterable of a tag's children.

for child in soup.head.children:
    print(child.name)

• Get the next sibling tag.

first_p = soup.find('p')
next_p = first_p.find_next_sibling('p')

• Get the previous sibling tag.

second_p = soup.find_all('p')[1]
prev_p = second_p.find_previous_sibling('p')

III. Finding Elements with BeautifulSoup

• Find the first occurrence of a tag.

first_link = soup.find('a')

• Find all occurrences of a tag.

all_links = soup.find_all('a')

• Find tags by their CSS class.

articles = soup.find_all('div', class_='article-content')

• Find a tag by its ID.

main_content = soup.find(id='main-container')

• Find tags by other attributes.

images = soup.find_all('img', attrs={'data-src': True})

• Find using a list of multiple tags.

headings = soup.find_all(['h1', 'h2', 'h3'])

• Find using a regular expression.

import re
links_with_blog = soup.find_all('a', href=re.compile(r'blog'))

• Find using a custom function.

# Finds tags with a 'class' but no 'id'
tags = soup.find_all(lambda tag: tag.has_attr('class') and not tag.has_attr('id'))

• Limit the number of results.

first_five_links = soup.find_all('a', limit=5)

• Use CSS Selectors to find one element.

footer = soup.select_one('#footer > p')

• Use CSS Selectors to find all matching elements.

article_links = soup.select('div.article a')

• Select direct children using CSS selector.

nav_items = soup.select('ul.nav > li')

IV. Extracting Data with BeautifulSoup

• Get the text content from a tag.

noscript_text = soup.noscript.get_text()

• Get stripped text content.

link_text = soup.find('a').get_text(strip=True)

• Get all text from the entire document.

all_text = soup.get_text()

• Get an attribute's value (like a URL).

link_url = soup.find('a')['href']

• Get the tag's name.

tag_name = soup.find('h1').name

• Get all attributes of a tag as a dictionary.

attrs_dict = soup.find('img').attrs

V. Parsing with lxml and XPath

• Import the library.

from lxml import html

• Parse HTML content with lxml.

tree = html.fromstring(response.content)

• Select elements using an XPath expression.

# Selects all <a> tags inside <div> tags with class 'nav'
links = tree.xpath('//div[@class="nav"]/a')

• Select text content directly with XPath.

# Gets the text of all <h1> tags
h1_texts = tree.xpath('//h1/text()')

• Select an attribute value with XPath.

# Gets all href attributes from <a> tags
hrefs = tree.xpath('//a/@href')

VI. Handling Dynamic Content (Selenium)

• Import the webdriver.

from selenium import webdriver

• Initialize a browser driver.

driver = webdriver.Chrome() # Requires chromedriver

• Navigate to a webpage.

driver.get('http://example.com')

• Find an element by its ID.

element = driver.find_element('id', 'my-element-id')

• Find elements by CSS Selector.

elements = driver.find_elements('css selector', 'div.item')

• Find an element by XPath.

button = driver.find_element('xpath', '//button[@type="submit"]')

• Click a button.

button.click()

• Enter text into an input field.

search_box = driver.find_element('name', 'q')
search_box.send_keys('Python Selenium')

• Wait for an element to become visible.

from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

element = WebDriverWait(driver, 10).until(
    EC.presence_of_element_located((By.ID, "myDynamicElement"))
)

• Get the page source after JavaScript has executed.

dynamic_html = driver.page_source

• Close the browser window.

driver.quit()

VII. Common Tasks & Best Practices

• Handle pagination by finding the "Next" link.

next_page_url = soup.find('a', text='Next')['href']

• Save data to a CSV file.

import csv
with open('data.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.writer(f)
    writer.writerow(['Title', 'Link'])
    # writer.writerow([noscript, url]) in a loop

• Save data to CSV using pandas.

import pandas as pd
df = pd.DataFrame(data, columns=['Title', 'Link'])
df.to_csv('data.csv', index=False)

• Use a proxy with requests.

proxies = {'http': 'http://10.10.1.10:3128', 'https': 'http://10.10.1.10:1080'}
requests.get('http://example.com', proxies=proxies)

• Pause between requests to be polite.

import time
time.sleep(2) # Pause for 2 seconds

• Handle JSON data from an API.

json_response = requests.get('https://api.example.com/data').json()

• Download a file (like an image).

img_url = 'http://example.com/image.jpg'
img_data = requests.get(img_url).content
with open('image.jpg', 'wb') as handler:
    handler.write(img_data)

• Parse a sitemap.xml to find all URLs.

# Get the sitemap.xml file and parse it like any other XML/HTML to extract <loc> tags.

VIII. Advanced Frameworks (Scrapy)

• Create a Scrapy spider (conceptual command).

scrapy genspider example example.com

• Define a parse method to process the response.

# In your spider class:
def parse(self, response):
    # parsing logic here
    pass

• Extract data using Scrapy's CSS selectors.

noscripts = response.css('h1::text').getall()

• Extract data using Scrapy's XPath selectors.

links = response.xpath('//a/@href').getall()

• Yield a dictionary of scraped data.

yield {'noscript': response.css('noscript::text').get()}

• Follow a link to parse the next page.

next_page = response.css('li.next a::attr(href)').get()
if next_page is not None:
    yield response.follow(next_page, callback=self.parse)

• Run a spider from the command line.

scrapy crawl example -o output.json

• Pass arguments to a spider.

scrapy crawl example -a category=books

• Create a Scrapy Item for structured data.

import scrapy
class ProductItem(scrapy.Item):
    name = scrapy.Field()
    price = scrapy.Field()

• Use an Item Loader to populate Items.

from scrapy.loader import ItemLoader
loader = ItemLoader(item=ProductItem(), response=response)
loader.add_css('name', 'h1.product-name::text')

#Python #WebScraping #BeautifulSoup #Selenium #Requests

━━━━━━━━━━━━━━━
By: @DataScienceN ✨

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