Added scripts for bulk download and scrapping

scripts/bulkdownloadfromurls.py

@@ -0,0 +1,67 @@
+import os
+import requests
+from urllib.parse import urlsplit, quote_plus
+import pandas as pd
+
+
+def read_urls_from_csv(csv_file, column_name):
+    try:
+        # Read CSV file into a DataFrame
+        df = pd.read_csv(csv_file)
+
+        # Extract URLs from specified column
+        urls = df[column_name].tolist()
+
+        return urls
+    except Exception as e:
+        print(f"Error reading URLs from CSV: {e}")
+        return []
+
+
+def download_image(url, folder):
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url, stream=True)
+        response.raise_for_status()  # Check if the request was successful
+
+        # Generate a unique filename using the URL
+        filename = quote_plus(url)  # Encode URL to use as filename
+        filename = filename[:25]  # Limit filename length (optional)
+        filename = f"{filename}.jpg"  # Add file extension if needed
+
+        # Create the output path
+        output_path = os.path.join(folder, filename)
+
+        # Save the image to the specified folder
+        with open(output_path, "wb") as file:
+            for chunk in response.iter_content(8192):
+                file.write(chunk)
+
+        print(f"Downloaded: {url} to {output_path}")
+    except requests.exceptions.RequestException as e:
+        print(f"Failed to download {url}: {e}")
+
+
+def download_images_from_list(url_list, folder):
+    # Create the output folder if it doesn't exist
+    os.makedirs(folder, exist_ok=True)
+
+    for url in url_list:
+        download_image(url, folder)
+
+
+if __name__ == "__main__":
+    # CSV file containing URLs
+    csv_file = "your_csv"
+    column_name = (
+        "YOUR COLUMN LINK CONTAING URLS"  # Replace with the column name containing URLs
+    )
+
+    # Read URLs from CSV
+    image_urls = read_urls_from_csv(csv_file, column_name)
+
+    # Folder to save downloaded images
+    output_folder = "downloaded_images"
+
+    # Download images
+    download_images_from_list(image_urls, output_folder)

scripts/simplescrapper.py

@@ -0,0 +1,114 @@
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+
+
+def scrape_categories(url):
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url)
+        response.raise_for_status()  # Check for request errors
+
+        # Parse the HTML content using BeautifulSoup
+        soup = BeautifulSoup(response.content, "html.parser")
+
+        # Find all category elements (adjust based on the website structure)
+        categories = []
+        for category in soup.find_all(
+            "a", class_="category-link"
+        ):  # Adjust class or tag based on the website structure
+            category_name = category.text.strip()
+            category_url = category["href"]
+            categories.append({"Category": category_name, "URL": category_url})
+
+        return categories
+    except Exception as e:
+        print(f"An error occurred while scraping categories: {e}")
+        return []
+
+
+def scrape_products(category_url):
+    try:
+        # Send a GET request to the category URL
+        response = requests.get(category_url)
+        response.raise_for_status()  # Check for request errors
+
+        # Parse the HTML content using BeautifulSoup
+        soup = BeautifulSoup(response.content, "html.parser")
+
+        # Find all product elements (adjust based on the website structure)
+        products = []
+        for product in soup.find_all(
+            "div", class_="product-item"
+        ):  # Adjust class or tag based on the website structure
+            product_name = product.find(
+                "h2", class_="product-name"
+            ).text.strip()  # Adjust based on the website structure
+            product_price = product.find(
+                "span", class_="product-price"
+            ).text.strip()  # Adjust based on the website structure
+            product_url = product.find("a", class_="product-link")[
+                "href"
+            ]  # Adjust based on the website structure
+            products.append(
+                {
+                    "Product Name": product_name,
+                    "Price": product_price,
+                    "URL": product_url,
+                }
+            )
+
+        return products
+    except Exception as e:
+        print(f"An error occurred while scraping products: {e}")
+        return []
+
+
+def save_to_excel(categories, output_file):
+    try:
+        # Create a DataFrame from the categories list
+        df = pd.DataFrame(categories)
+
+        # Save the DataFrame to an Excel file
+        df.to_excel(output_file, index=False)
+        print(f"Categories and products saved to {output_file}")
+    except Exception as e:
+        print(f"An error occurred while saving to Excel: {e}")
+
+
+if __name__ == "__main__":
+    # Input: E-commerce website URL
+    url = input("Enter the e-commerce website URL: ").strip()
+
+    # Output: Excel file name
+    output_file = "categories_and_products.xlsx"
+
+    # Scrape categories
+    categories = scrape_categories(url)
+
+    # Scrape products for each category
+    for category in categories:
+        category_url = category["URL"]
+        products = scrape_products(category_url)
+        for product in products:
+            product["Category"] = category[
+                "Category"
+            ]  # Add category name to each product
+            category["Products"] = products
+
+    # Flatten the data for saving to Excel
+    all_data = []
+    for category in categories:
+        for product in category.get("Products", []):
+            all_data.append(
+                {
+                    "Category": category["Category"],
+                    "Product Name": product["Product Name"],
+                    "Price": product["Price"],
+                    "Product URL": product["URL"],
+                    "Category URL": category["URL"],
+                }
+            )
+
+    # Save to Excel
+    save_to_excel(all_data, output_file)