from openai import OpenAI
import anthropic
from tqdm import tqdm
from urllib.parse import urlparse
from requests.auth import HTTPBasicAuth
import mimetypes
import ast
from PIL import Image
import re
import unicodedata
from eventregistry import *
import json
from fuzzywuzzy import fuzz
from datetime import datetime, timedelta
import time

def is_similar_title(title1, title2, threshold=70):
    """
    Check if two titles are similar based on a similarity threshold.

    Args:
        title1 (str): First title.
        title2 (str): Second title.
        threshold (int): Similarity threshold (default 85%).

    Returns:
        bool: True if titles are similar, False otherwise.
    """
    return fuzz.token_sort_ratio(title1, title2) >= threshold


def get_news_articles(date_start, date_end, max_items=100):
    """
    Fetch news articles based on location and date range, filtering out similar titles.

    Args:
        location_uri (str): The URI of the location (e.g., country).
        date_start (str): The start date for fetching articles (format: YYYY-MM-DD).
        date_end (str): The end date for fetching articles (format: YYYY-MM-DD).
        max_items (int): Maximum number of articles to fetch. Default is 100.

    Returns:
        list: A list of unique or sufficiently distinct articles fetched based on the query.
    """
    # Define the query with input parameters
    query = {
        "$query": {
            "$and": [
                {
                    "$or": [
                        {"conceptUri": "http://en.wikipedia.org/wiki/Agriculture"},
                        {"conceptUri": "http://en.wikipedia.org/wiki/Farming"},
                        {
                            "conceptUri": "http://en.wikipedia.org/wiki/Forestry"
                        },
                        {
                            "conceptUri": "http://en.wikipedia.org/wiki/Agriculture"
                        },
                        {
                            "conceptUri": "http://en.wikipedia.org/wiki/Organic_farming"
                        },
                        {
                            "conceptUri": "http://en.wikipedia.org/wiki/Fertilizer"
                        },
                        {
                            "conceptUri": "http://en.wikipedia.org/wiki/Agribusiness"
                        }
                    ]
                },
                {"categoryUri": "dmoz/Business/Agriculture_and_Forestry"},
                {"$or":[{
                            "locationUri": "http://en.wikipedia.org/wiki/United_States"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/United_Kingdom"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/Australia"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/Europe"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/Canada"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/Africa"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/South_America"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/Middle_East"
                        },
                        {
                            "locationUri": "http://en.wikipedia.org/wiki/Asia"
                        }]},
                {
                    "dateStart": date_start,  # Use the input dateStart
                    "dateEnd": date_end,  # Use the input dateEnd
                },
            ]
        }
    }

    # Initialize query
    q = QueryArticlesIter.initWithComplexQuery(query)

    # List to store unique/sufficiently distinct articles
    articles_list = []
    seen_titles = []  # List to track seen titles (for fuzzy matching)

    # Fetch and append unique articles to the list
    for article in q.execQuery(er, maxItems=max_items):
        title = article.get("title", None)
        if title:
            # Check for duplicates or highly similar titles using fuzzy matching
            if not any(
                is_similar_title(title, seen_title) for seen_title in seen_titles
            ):
                articles_list.append(article)
                seen_titles.append(title)  # Track the title for similarity comparison

    return articles_list


def create_slug(title):
    # Normalize the string and remove accents
    title = (
        unicodedata.normalize("NFKD", title).encode("ascii", "ignore").decode("utf-8")
    )

    # Convert to lowercase
    title = title.lower()

    # Remove special characters and replace spaces with hyphens
    title = re.sub(
        r"[^a-z0-9\s-]", "", title
    )  # Keep only alphanumeric characters, spaces, and hyphens
    title = re.sub(r"\s+", "-", title).strip(
        "-"
    )  # Replace spaces with hyphens and trim leading/trailing hyphens

    return title


def string_to_array(string):
    # Use ast.literal_eval to safely evaluate the string as a list
    try:
        array = ast.literal_eval(string)
        print(array)
    except:
        array = string
    return array


def call_openai(prompt, temperature, max_tokens):
    ## Initialize OpenAi
    api_key = "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"
    api_key = "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"
    client = OpenAI(api_key=api_key)

    completion = client.chat.completions.create(
        model="gpt-4o-mini-2024-07-18",
        messages=[
            {
                "role": "system",
                "content": "You are a expert in SEO and a representative of Farmonaut.",
            },
            {"role": "user", "content": prompt},
        ],
        max_tokens=max_tokens,
        temperature=temperature,
    )
    return completion.choices[0].message.content


def call_genai(prompt, temperature, max_tokens):
    client = anthropic.Anthropic(
        # defaults to os.environ.get("ANTHROPIC_API_KEY")
        api_key="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",
    )

    message = client.messages.create(
        model="claude-3-5-sonnet-20240620",
        max_tokens=max_tokens,
        temperature=temperature,
        system="You are an SEO expert, a gis/ remote sensing expert, an agriculture and horticulture scientist, and a representative of Farmonaut (farmonaut.com).",
        messages=[{"role": "user", "content": prompt}],
    )
    # print(message)
    return message.content[0].text


def ai_image_url(prompt):
    ## Initialize OpenAi
    api_key = "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"
    api_key = "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"
    client = OpenAI(api_key=api_key)

    response = client.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1024",
        quality="standard",
        n=1,
    )

    image_url = response.data[0].url
    return image_url


def get_file_extension(url):
    # Parse the URL
    parsed_url = urlparse(url)

    # Get the path component of the URL
    path = parsed_url.path

    # Extract the file extension
    file_extension = os.path.splitext(path)[1]

    # Return the extension (without the dot) or an empty string if there's no extension
    return file_extension[1:] if file_extension else ""


def add_watermark(main_image_path, watermark_path):
    # Open the main image
    main_image = Image.open(main_image_path).convert("RGBA")

    # Open the watermark image
    watermark = Image.open(watermark_path).convert("RGBA")

    # Calculate the new size for the watermark
    new_height = int(main_image.height * 0.1)
    aspect_ratio = watermark.width / watermark.height
    new_width = int(new_height * aspect_ratio)

    # Resize the watermark
    watermark = watermark.resize((new_width, new_height), Image.LANCZOS)

    # Calculate the position for the watermark (bottom right)
    # position = (main_image.width - watermark.width, main_image.height - watermark.height)
    position = (main_image.width - watermark.width, 0)

    # Create a new transparent image the same size as the main image
    transparent = Image.new("RGBA", main_image.size, (0, 0, 0, 0))

    # Paste the watermark onto the transparent image
    transparent.paste(watermark, position, watermark)

    # Combine the main image with the watermark
    output = Image.alpha_composite(main_image, transparent)

    # Convert back to the original mode if it wasn't RGBA
    original_image = Image.open(main_image_path)
    if original_image.mode != "RGBA":
        output = output.convert(original_image.mode)

    # Save the result, overwriting the original image
    output.save(main_image_path)

    print(f"Watermark added to {main_image_path}")


def convert_png_to_jpg(file_path):
    # Check if the file exists and is a PNG
    if not os.path.isfile(file_path) or not file_path.lower().endswith(".png"):
        raise ValueError("The provided file is not a valid PNG file.")

    # Open the PNG image
    with Image.open(file_path) as img:
        # Get the file name without extension
        file_name = os.path.splitext(file_path)[0]

        # Convert to RGB if the image has an alpha channel
        if img.mode in ("RGBA", "LA") or (
            img.mode == "P" and "transparency" in img.info
        ):
            img = img.convert("RGB")

        # Save as JPG
        jpg_path = f"{file_name}.jpg"
        img.save(jpg_path, "JPEG")

    # Remove the original PNG file
    os.remove(file_path)

    print(f"Converted {file_path} to {jpg_path} and removed the original PNG.")


def upload_media_to_wordpress(file_path, title):
    # WordPress credentials
    wp_url = "https://www.farmonaut.com"
    wp_username = "ankuromar296"
    wp_password = "Tjat A2hz 9XMv pXJi YbV0 GR8o"

    endpoint = f"{wp_url}/wp-json/wp/v2/media"
    auth = HTTPBasicAuth(wp_username, wp_password)

    mime_type, _ = mimetypes.guess_type(file_path)
    media_data = {"alt_text": title, "caption": title, "description": title}
    upload_name = f"{title}_{os.path.basename(file_path)}"
    with open(file_path, "rb") as file:
        files = {"file": (upload_name, file, mime_type)}
        # files = {'file': (os.path.basename(file_path), file, mime_type)}
        response = requests.post(endpoint, files=files, auth=auth, json=media_data)

    if response.status_code == 201:
        return response.json()["id"], response.json()["source_url"]
    else:
        print(f"Failed to upload media. Status code: {response.status_code}")
        print(f"Response: {response.text}")
        return None, None


def publish_or_update_wordpress_post(post_data):
    # WordPress credentials
    wp_url = "https://www.farmonaut.com"
    wp_username = "ankuromar296"
    wp_password = "Tjat A2hz 9XMv pXJi YbV0 GR8o"

    # WordPress REST API endpoints
    posts_url = f"{wp_url}/wp-json/wp/v2/posts"

    # Set up authentication
    auth = HTTPBasicAuth(wp_username, wp_password)

    # Check if the API is accessible
    try:
        response = requests.get(f"{wp_url}/wp-json", auth=auth)
        response.raise_for_status()
    except requests.exceptions.RequestException as e:
        raise Exception(f"Failed to access WordPress API: {str(e)}")

    # Check if a post with the same title exists
    # existing_post = check_existing_post_by_title(site_url, auth, post_data['title'])

    # if existing_post:
    #     # Update existing post
    #     update_url = f"{posts_url}/{existing_post['id']}"
    #     post_data['id'] = existing_post['id']
    #     response = requests.post(update_url, json=post_data, auth=auth)
    # else:
    # Create new post
    response = requests.post(posts_url, json=post_data, auth=auth)

    if response.status_code in [200, 201]:
        return response.json()
    else:
        raise Exception(f"Failed to publish/update post: {response.text}")


def process_media2(media_url_arr, title):
    # print(media_url_arr)
    media_info = []
    media_num = 0
    folder_name = "insta_files_news"
    for url in media_url_arr:
        # print(url)
        media_num = media_num + 1

        file_path = (
            folder_name + "/" + str(media_num) + "." + str(get_file_extension(url))
        )
        try:
            response = requests.get(url, stream=True)
        except:
            print(traceback.format_exc())
            response = None
        if response is None:
            continue
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        if response.status_code == 200:
            with open(file_path, "wb") as file:
                for chunk in response.iter_content(chunk_size=8192):
                    if chunk:
                        file.write(chunk)
            print(f"File downloaded successfully: {file_path}")
            add_watermark(file_path, "watermark.jpg")
            convert_png_to_jpg(file_path)
        else:
            print(f"Failed to download file. Status code: {response.status_code}")

    # with tempfile.TemporaryDirectory() as tmpdir:
    #     for media_url in media_url_arr:

    #     L.download_post(post, target=tmpdir)
    media_files = os.listdir(folder_name)

    for file in media_files:
        file_path = os.path.join(folder_name, file)

        media_id, media_url = upload_media_to_wordpress(file_path, title)
        if media_id and media_url:
            media_info.append(
                {"type": "image", "text": title, "id": media_id, "url": media_url}
            )
        else:
            continue  # Skip files that are neither videos nor images

    return media_info


def create_breadcrumbs_fromURL(parsed_url):

    # Split the path into segments
    path_segments = parsed_url.path.strip("/").split("/")  # Split by '/'

    # Construct breadcrumb items
    breadcrumbs = {"@type": "BreadcrumbList", "itemListElement": []}

    # Add the home item
    breadcrumbs["itemListElement"].append(
        {
            "@type": "ListItem",
            "position": 1,
            "name": "Home",
            "item": f"{parsed_url.scheme}://{parsed_url.netloc}/",  # Base URL
        }
    )

    # Add items for each segment in the path
    for position, segment in enumerate(path_segments, start=2):  # Start at position 2
        breadcrumb_item = {
            "@type": "ListItem",
            "position": position,
            "name": segment.replace(
                "-", " "
            ).title(),  # Convert hyphens to spaces and capitalize
            "item": f"{parsed_url.scheme}://{parsed_url.netloc}/{'/'.join(path_segments[:position-1])}",  # Construct the URL up to this segment
        }
        breadcrumbs["itemListElement"].append(breadcrumb_item)

    return breadcrumbs


def create_news_schema(title, media_url_arr, blog_url):

    # Extract image URLs
    image_urls = list(media_url_arr)  ## make sure it is a list

    # Organizatio details
    organization = (
        {
            "@type": "Organization",
            "name": "Farmonaut",
            "url": "https://farmonaut.com",
            "sameAs": [
                "https://www.facebook.com/farmonaut",
                "https://twitter.com/farmonaut",
                "https://www.linkedin.com/company/farmonaut",
                "https://www.instagram.com/farmonaut",
                # Add other social media links as necessary
            ],
        },
    )

    breadcrumbs = create_breadcrumbs_fromURL(urlparse(blog_url))

    # Get current timestamp in ISO 8601 format
    current_timestamp = datetime.utcnow().isoformat() + "Z"

    return {
        "@context": "https://schema.org",
        "@type": "NewsArticle",
        "headline": title,
        "image": image_urls,
        "author": organization,
        "breadcrumb": breadcrumbs,
        "datePublished": current_timestamp,
    }


def get_news_summary(title, content):
    ## Get SEO Optimized Summary
    total_tokens = int(len(content)/4)
    prompt = f"""Rewrite this text in roughly {total_tokens} tokens : 
    Text: {content}
    Note: Do not add any additional information whatsoever."""
    # prompt = f"""Summarize the content of this News Article in a highly SEO-optimized way, keeping it under 200 words. 
    # Include key points that would appeal to a reader and potential search engines: 
    # Title: {title}, Content: {content}
    # """
    summary = call_openai(prompt, temperature=1, max_tokens=8000)

    return summary


def get_keywords(title, content):
    ## Get SEO Optimized Keywords
    prompt = f"""From the content and summary provided, extract 5-10 SEO-optimized keywords that are highly relevant to the news topic:  
    Title: {title}, Content: {content}
    Output format:
    - Return only the refined array of keywords
    - Do not include any explanatory text or commentary """
    keywords = call_genai(prompt, temperature=1, max_tokens=1000)
    keywords = string_to_array(keywords)

    return keywords


def generate_title(keywords, summary):

    ## Get SEO Optimized Title
    prompt = f"""Using the following keywords and summary, create a catchy SEO-optimized title for the news article. 
    Make it 150 characters or less and include at least one power word. Keywords: {keywords}. Summary: {summary}
    4. Output format:
        - Return only the suggested title
        - Do not include any explanatory text, quotation marks, or additional commentary"""

    title = call_genai(prompt, temperature=1, max_tokens=1000)

    return title


def get_keyphrases(title, keywords, summary):
    ## Get SEO Optimized KeyPhrases
    prompt = f"""Using the following title, keywords, and summary, generate a set of 5-7 SEO-friendly keyphrases. 
    Ensure the keyphrases are relevant and likely to rank well in search engines. 
    Title: {title}, Keywords: {keywords}, Summary: {summary}
    3. Output format:
    - Return results as an array of strings
    - Each keyphrase should be its own element in the array
    - Do not include any explanatory text or commentary
    - If fewer than 10 suitable keyphrases are found, include only those that meet the criteria

    Example output format:
    ["keyphrase 1", "keyphrase 2", "keyphrase 3", ...]"""
    keyphrases = call_genai(prompt, temperature=1, max_tokens=1000)
    keyphrases = string_to_array(keyphrases)

    return keyphrases


def generate_summary(keywords, keyphrases, summary):
    #return summary
    ## Rewrite Summary - SEO Optimized
    prompt = f"""Task: Generate an SEO-optimized news blog's meta description:

    Inputs:
        - Keywords: {keywords}
        - Key phrases: {keyphrases}
        - Reference Text: {summary}

    Output format:
        - Provide only the 150-word summary
        - DO NOT OUTPUT ANY OTHER TEXT WITH THE RESPONSE
        """
    summary = call_genai(prompt, 0, 1000)
    return summary


def generate_images(keywords, title, summary, keyphrases):

    ## Get Image Description
    prompt = f"""
        Task: Suggest two SEO-optimizing AI image descriptions (DALL-E 3) for News Article.

        Inputs:
        a. Keywords: {keywords}
        b. Title: {title}
        c. Context: {summary}
        d. KeyPhrases: {keyphrases}
        
    Output format:
                        [
                        "Detailed description of first image, incorporating relevant keywords and focusing on a key aspect of the blog topic or Farmonaut's services.",
                        "Detailed description of second image, highlighting a different facet of the blog content or Farmonaut's technology, using appropriate keywords."
                        ]

                        Note: Focus on creating descriptions that would result in images that add significant value to the blog post while optimizing for search engines. Ensure descriptions are distinct from each other and highly relevant to the content. DO NOT OUTPUT ANY OTHER TEXT WITH THE RESPONSE.
        """
    image_descriptions = call_genai(prompt, 1, 1000)
    print("\nImage Descriptions:")
    image_descriptions = string_to_array(image_descriptions)

    ## Generate Images
    media_url_arr = []

    for image_description in image_descriptions:
        try:
            media_url_arr.append(ai_image_url(image_description))
            print("media url", media_url_arr)
        except:
            print(traceback.format_exc())

    return media_url_arr


def generate_news_article(title, keywords, keyphrases, summary, media):
    ## Generate News Article
    prompt = f"""Using the title, keywords, keyphrases, and summary, write a news article of upto 1500 words. 
    Make sure the article is SEO-optimized, informative, engaging, and integrates the keyphrases naturally. 
    Title: {title}, Keywords: {keywords}, Keyphrases: {keyphrases}, Summary: {summary}

    Key requirements:
    - Use HTML formatting including <strong>, <h1>, <h2>, <i>, <u>, <ol>, <ul>, <br>, and <p> tags where appropriate
    - Include and naturally incorporate as many of these keywords: {keywords}
    - Include these key phrases: {keyphrases}
    - Write in the language of keywords/keywords if keywords/phrases are not in English
    - Mandatorily Localize the content if location names are available in keywords.
    - Add all the images from this JSON object: {media} with border-radius:16px, box-shadow: 10px 10px 15px, cursor: pointer. These images should open https://farmonaut.com/app_redirect when clicked. All images should be placed within 75% content of the blog.
    - Add links:
    - App Button Image: https://farmonaut.com/Images/web_app_button.png, Link on this button: https://farmonaut.com/app_redirect, height: 80px
    - API: https://sat.farmonaut.com/api
    - API Developer Docs:https://farmonaut.com/farmonaut-satellite-weather-api-developer-docs/
    - Android App Button Image: https://farmonaut.com/wp-content/uploads/2020/01/get_it_on_google_play.png, Link on this Button: https://play.google.com/store/apps/details?id=com.farmonaut.android, height: 80px
    - iOS App Button Image: https://farmonaut.com/wp-content/uploads/2020/01/available_on_app_store.png, Link on this Button: https://apps.apple.com/in/app/farmonaut/id1489095847, height: 80px
    - Distribute these links within top 75% content of the blog with bold font
    - Use bullet points and subheadings (font color: #034d5c) to improve readability
    - Make the content mobile responsive

    Additional guidelines:
    - Achieve Flesch Reading Ease Score of at least 60
    - Provide detailed explanations and examples
    - Ensure all content is factual and based on provided information
    - Organize information logically with clear transitions between sections
    - Use varied sentence structures and vocabulary for engaging reading
    - Mandatorily implement all the latest SEO guidelines provided by Google News

    Please generate the news article  based on these requirements, ensuring it's well-structured, and upto 1500 words long.

    """

    news_content = call_genai(prompt, temperature=1, max_tokens=8000)
    return news_content


def generate_and_publish_news_article(article):
    # Extract the article content (or provide a default value if not available)
    title_article = article.get("title", "No title available")
    content_article = article.get("body", "No content available")
    news_summary = content_article
    summary = news_summary

    # ## Get News article Data
    # news_summary = get_news_summary(title_article, content_article)
    # print("Getting keywords")
    keywords = get_keywords(title_article, content_article)

    title = generate_title(keywords, news_summary)
    print("title", title)

    print("Getting keyphrases")
    keyphrases = get_keyphrases(title, keywords, news_summary)

    excerpt = generate_summary(keywords, keyphrases, news_summary)
    # print("Summary Generated :- ", summary)

    ## Generate Images
    media_url_arr = generate_images(keywords, title, summary, keyphrases)
    media_info = process_media2(media_url_arr, title)
    print("Media Processed")

    ##
    post_data = {"caption": excerpt, "media": []}
    if media_info:
        post_data["media"].extend(media_info)
        post_data["featured_media"] = media_info[0]["id"] if media_info else None

    media = post_data["media"]
    news_content = generate_news_article(title, keywords, keyphrases, summary, media)
    print("news content processed \n")

    ## blog URL
    blog_url = "https://farmonaut.com/news/" + create_slug(title)

    ## Generate Schema
    image_urls = [item["url"] for item in media_info if item["type"] == "image"]
    structured_schema = create_news_schema(title, image_urls, blog_url)
    # Convert structured data to JSON-LD format
    structured_schema_script = (
        f'<script type="application/ld+json">{json.dumps(structured_schema)}</script>'
    )

    post_data["title"] = title
    post_data["content"] = structured_schema_script + news_content
    post_data["status"] = "publish"
    post_data["excerpt"] = excerpt
    post_data["comment_status"] = "open"
    post_data["categories"] = [573]  ## need to hard code for news

    ## Publish to website
    publish_or_update_wordpress_post(post_data)

    return post_data


def publish_news_spaced(articles):
    """
    Publish news articles spaced equally over 24 hours.

    Args:
        articles (list): List of articles to publish.
        generate_and_publish_news_article (func): Function to generate and publish an article.
    """
    total_articles = len(articles)
    if total_articles == 0:
        print("No articles to publish.")
        return

    # Calculate time interval in seconds
    interval = (
        24 * 3600 / total_articles
    )  # 24 hours in seconds, divided by the number of articles

    for article in tqdm(articles, desc="Processing News articles"):
        try:
            generate_and_publish_news_article(article)  # Generate and publish the article
            time.sleep(
                interval
            )  # Wait for the calculated interval before publishing the next article
        except:
            print(traceback.format_exc())

while True:
####### News APIKEY #######
    APIKEY = "ae5e326f-a428-41d3-b0c8-09f1746f98b1"

    ## Initailize News Api
    er = EventRegistry(apiKey=APIKEY, allowUseOfArchive=False)

    ### Usage
    #location = "http://en.wikipedia.org/wiki/United_States"

    # start_date = "2024-10-12"  # Example start date
    # end_date = "2024-10-13"  # Example end date
    # Get today's date and yesterday's date
    end_date = datetime.today().strftime("%Y-%m-%d")
    start_date = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")

    # Fetch articles
    articles = get_news_articles(start_date, end_date, max_items=100)

    # Print the number of articles fetched
    print(f"Number of articles fetched: {len(articles)}")

    publish_news_spaced(articles)
    time.sleep(2*60*60)