feat: add conversion script

Author: GabrielePicco

md_to_csv.py

@@ -0,0 +1,147 @@
+import os
+import csv
+import re
+import markdown
+import random
+from datetime import datetime, timedelta
+from pathlib import Path
+import yaml
+from openai import OpenAI
+import textwrap
+
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# Set the folder with markdown files
+MARKDOWN_FOLDER = "./_posts"
+OUTPUT_CSV = "converted_blogposts.csv"
+
+# Set static values (you can customize)
+COLLECTION_ID = "662a1b809adeb156702a5d75"
+LOCALE_ID = "662a1b8028d7e78401160d02"
+ITEM_ID = "PLACEHOLDER_ITEM_ID"
+
+def generate_short_description(title, content, max_length=150):
+    """Generate a short description using OpenAI's API"""
+    if not OPENAI_API_KEY:
+        return ""
+
+    try:
+        prompt = f"""
+        Generate a concise and engaging summary of the following blog post in about 2-3 sentences.
+        The summary should highlight the main points and be less than 150 characters if possible.
+        It should be suitable for use in a blog post's metadata. It shouldn't contain hashtags or links.
+
+        Title: {title}
+        Content: {textwrap.shorten(content, width=1000, placeholder="...")}
+        """
+
+        response = client.chat.completions.create(model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a skilled blog editor who writes concise, engaging summaries."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=100,
+        temperature=0.7)
+
+        description = response.choices[0].message.content.strip()
+
+        return description
+    except Exception as e:
+        print(f"Error generating description: {e}")
+        return ""
+
+def parse_markdown_file(filepath):
+    with open(filepath, "r", encoding="utf-8") as f:
+        content = f.read()
+
+    # Split front matter
+    front_matter_match = re.match(r"---\n(.*?)\n---\n(.*)", content, re.DOTALL)
+    if not front_matter_match:
+        raise ValueError(f"Front matter not found in {filepath}")
+
+    front_matter = yaml.safe_load(front_matter_match.group(1))
+    body_md = front_matter_match.group(2)
+
+    body_html = markdown.markdown(body_md)
+
+    return front_matter, body_md, body_html
+
+def generate_csv_row(filepath, front_matter, body_md, html_content):
+    filename = Path(filepath).stem
+
+    # Fix the date parsing - extract complete date from filename
+    # Instead of splitting at first hyphen, extract the full YYYY-MM-DD part
+    match = re.match(r'(\d{4}-\d{2}-\d{2})-(.*)', filename)
+    if match:
+        date_str = match.group(1)
+        slug = match.group(2)
+    else:
+        # Fallback if pattern doesn't match
+        date_parts = filename.split('-', 3)[:3]  # Get first 3 parts
+        date_str = '-'.join(date_parts)
+        slug = '-'.join(filename.split('-')[3:])
+
+    post_date = datetime.strptime(date_str, "%Y-%m-%d")
+    formatted_date = post_date.strftime("%a %b %d %Y 00:00:00 GMT+0000 (Coordinated Universal Time)")
+
+    # Create timestamps with small random hour variations based on the post date
+    created_date = post_date.replace(hour=random.randint(8, 11), minute=random.randint(0, 59))
+    updated_date = post_date.replace(hour=random.randint(12, 15), minute=random.randint(0, 59))
+    published_date = post_date.replace(hour=random.randint(16, 18), minute=random.randint(0, 59))
+
+    created_str = created_date.strftime("%a %b %d %Y %H:%M:%S GMT+0000 (Coordinated Universal Time)")
+    updated_str = updated_date.strftime("%a %b %d %Y %H:%M:%S GMT+0000 (Coordinated Universal Time)")
+    published_str = published_date.strftime("%a %b %d %Y %H:%M:%S GMT+0000 (Coordinated Universal Time)")
+
+    # Generate short description
+    title = front_matter.get("title", "")
+    short_description = generate_short_description(title, body_md)
+
+    return [
+        title,                          # Name
+        slug,                           # Slug
+        COLLECTION_ID,                  # Collection ID
+        LOCALE_ID,                      # Locale ID
+        ITEM_ID,                        # Item ID (placeholder)
+        created_str,                    # Created On - aligned with post date
+        updated_str,                    # Updated On - aligned with post date
+        published_str,                  # Published On - aligned with post date
+        f"https://blog.magicblock.gg/{front_matter.get('image')}",  # Cover - changed domain
+        short_description,              # Short Description generated by ChatGPT
+        formatted_date,                 # Date
+        html_content,                   # Main Content (HTML)
+        "true",                         # Main Post
+        "false",                        # Home Post
+        "false"                         # Top Post
+    ]
+
+def convert_all_markdown_to_csv():
+    files = [f for f in os.listdir(MARKDOWN_FOLDER) if f.endswith(".md")]
+    rows = []
+
+    for file in files:
+        path = os.path.join(MARKDOWN_FOLDER, file)
+        try:
+            front_matter, body_md, html_content = parse_markdown_file(path)
+            row = generate_csv_row(path, front_matter, body_md, html_content)
+            rows.append(row)
+        except Exception as e:
+            print(f"Error processing {file}: {e}")
+
+    headers = [
+        "Name", "Slug", "Collection ID", "Locale ID", "Item ID", "Created On",
+        "Updated On", "Published On", "Cover", "Short Description", "Date",
+        "Main Content", "Main Post", "Home Post", "Top Post"
+    ]
+
+    with open(OUTPUT_CSV, "w", encoding="utf-8", newline="") as f:
+        writer = csv.writer(f)
+        writer.writerow(headers)
+        writer.writerows(rows)
+
+    print(f"Conversion completed. CSV saved to: {OUTPUT_CSV}")
+
+if __name__ == "__main__":
+    convert_all_markdown_to_csv()