import re
import os
import time
import base64
import requests
import dotenv
import mistune

dotenv.load_dotenv()
access_key = os.getenv('ACCESS_KEY')
client_id = os.getenv('CLIENT_ID')
client_secret = os.getenv('CLIENT_SECRET')
refresh_token = os.getenv('REFRESH_TOKEN')
blog_id = os.getenv('BLOG_ID')

def extract_summary(text):
    text = text.replace("#", "").strip().lower()
    match = re.search(r"summary(.*?)highlights", text.replace("#", ""), re.DOTALL)
    if match:
        return match.group(1).replace("#", "").replace("\n", "").strip()
    return None

def generate_image(title, summary, category):
    extracted_summary = extract_summary(summary)
    if extracted_summary is None:
        extracted_summary = title
    url = f"https://image.pollinations.ai/prompt/{title}%20%3A%20{extracted_summary}?width=1280&height=720&seed=623862700&nologo=true&model=turbo"
    requests.get(url).content
    return url

def generate_post_html(title, summary, mindmap, category, citation):
    title = title.replace("{", r"{{").replace("}", r"}}")
    summary = summary.replace("{", r"{{").replace("}", r"}}")
    mindmap = mindmap.replace("{", r"{{").replace("}", r"}}")
    citation = citation.replace("{", r"{{").replace("}", r"}}")
    image = generate_image(title, summary, category)
    html_summary = mistune.html(summary)
    post = f"""
<div>
<script src="https://cdn.jsdelivr.net/npm/markmap-autoloader@latest"></script>
<style>
.markmap {{
position: relative;
}}
.markmap > svg {{
width: 100%;
border: 2px solid #000;
height: 80dvh;
}}
</style>
<img style='display:block; width:100%;height:100%;' id='paper_image' src='{image}' />
<br>
<p id="paper_summary" data="{summary.replace("&amp;", "&")}">{html_summary.replace("&amp;", "&")}</p>
<br>
<br>
<h2>Mindmap</h2>
<div class="markmap"  id="paper_mindmap" data="# {title} \n {mindmap.replace("&amp;", "&")}">
<script type="text/template">
{mindmap.replace("&amp;", "&")}
</script>
</div>
<br>
<h2>Citation</h2>
<p id="paper_citation" data="{citation.replace("&amp;", "&")}">
{mistune.html(citation.replace("&amp;", "&"))}
</p>
</div>
    """
    return post, image

def create_post(title, category, summary, mindmap, citation):
    post_title = title
    post_category = f"{category}"
    post_body, post_image = generate_post_html(title, summary, mindmap, category, citation)
    return post_title, post_category, post_body, post_image

def post_post(title, category, body, image):
    data = None
    data = requests.post(
        url='https://oauth2.googleapis.com/token',
        data={
            'grant_type': 'refresh_token',
            'client_secret': client_secret,
            'refresh_token': refresh_token,
            'client_id': client_id,
        },
        ).json()
    url = f"https://blogger.googleapis.com/v3/blogs/{blog_id}/posts"
    headers = {
        'Authorization': f"Bearer {data['access_token']}",
        "content-type": "application/json"
    }
    post_data = {
        "kind": "blogger#post",
        "blog": {
            "id": blog_id
        },
        "images": [{
            "url": image
        }],
        "title": title,
        "content": body,
        "labels": [category, "recent"]
    }
    data = requests.post(url, headers=headers, json=post_data).json()
    if data['status'] == 'LIVE':
        print(f"The post '{title}' is {data['status']}")
        return True
    else:
        print(f"Error posting {title}: {data}")
        return False
    
def post_blog(title, category, summary, mindmap, citation, uaccess_key):
    if uaccess_key != access_key:
        return False
    else:
        post_title, post_category, post_body, post_image = create_post(title, category, summary, mindmap, citation)
        status = post_post(post_title, post_category, post_body, post_image)
        print(f"Waiting for {3*60} seconds...")
        time.sleep(3*60)
        if status:
            print('Post created successfully')
            return True
        else:
            print('Failed to create post')
            return False
    
def test(access_key):
    data = {
            "status": "success",
            "Astrophysics": {
                "2412.16344": {
                    "id": "2412.16344",
                    "doi": "https://doi.org/10.48550/arXiv.2412.16344",
                    "title": "On the Interplay of Constraints from $B_s$, $D$, and $K$ Meson Mixing in $Z^\\prime$ Models with Implications for $b\to s ν\barν$ Transitions",
                    "category": "Astrophysics",
                    "citation": "Grant, C. E., Bautz, M. W., Miller, E. D., Foster, R. F., LaMarr, B., Malonis, A., Prigozhin, G., Schneider, B., Leitz, C., &amp; Falcone, A. D. (2024). Focal Plane of the Arcus Probe X-Ray Spectrograph. ArXiv. https://doi.org/10.48550/ARXIV.2412.16344",
                    "summary": "## Summary\nThe Arcus Probe mission concept provides high-resolution soft X-ray and UV spectroscopy to study the universe. The X-ray Spectrograph (XRS) uses two CCD focal planes to detect and record X-ray photons. Laboratory performance results meet observatory requirements.\n\n## Highlights\n- The Arcus Probe mission concept explores the formation and evolution of clusters, galaxies, and stars.\n- The XRS instrument includes four parallel optical channels and two detector focal plane arrays.\n- The CCDs are designed and manufactured by MIT Lincoln Laboratory (MIT/LL).\n- The XRS focal plane utilizes high heritage MIT/LL CCDs with proven technologies.\n- Laboratory testing confirms CCID-94 performance meets required spectral resolution and readout noise.\n- The Arcus mission includes two co-aligned instruments working simultaneously.\n- The XRS Instrument Control Unit (XICU) controls the activities of the XRS.\n\n## Key Insights\n- The Arcus Probe mission concept provides a significant improvement in sensitivity and resolution over previous missions, enabling breakthrough science in understanding the universe.\n- The XRS instrument's design, including the use of two CCD focal planes and four parallel optical channels, allows for high-resolution spectroscopy and efficient detection of X-ray photons.\n- The CCDs used in the XRS instrument are designed and manufactured by MIT Lincoln Laboratory (MIT/LL), which has a proven track record of producing high-quality CCDs for space missions.\n- The laboratory performance results of the CCID-94 device demonstrate that it meets the required spectral resolution and readout noise for the Arcus mission, indicating that the instrument is capable of achieving its scientific goals.\n- The XRS Instrument Control Unit (XICU) plays a crucial role in controlling the activities of the XRS, including gathering and storing data, and processing event recognition.\n- The Arcus mission's use of two co-aligned instruments working simultaneously allows for a wide range of scientific investigations, including the study of time-domain science and the physics of time-dependent phenomena.\n- The high heritage MIT/LL CCDs used in the XRS focal plane provide a reliable and efficient means of detecting X-ray photons, enabling the instrument to achieve its scientific goals.",
                    "mindmap": "## Arcus Probe Mission Concept\n- Explores formation and evolution of clusters, galaxies, stars\n- High-resolution soft X-ray and UV spectroscopy\n- Agile response capability for time-domain science\n\n## X-Ray Spectrograph (XRS) Instrument\n- Two nearly identical CCD focal planes\n- Detects and records X-ray photons from dispersed spectra\n- Zero-order of critical angle transmission gratings\n\n## XRS Focal Plane Characteristics\n- Frametransfer X-ray CCDs\n- 8-CCD array per Detector Assembly\n- FWHM < 70 eV @ 0.5 keV\n- System read noise ≤ 4 e- RMS @ 625 kpixels/sec\n\n## Detector Assembly\n- Eight CCDs in a linear array\n- Tilted to match curved focal surface\n- Gaps minimized between CCDs\n- Alignment optimized with XRS optics\n\n## Detector Electronics\n- Programmable analog clock waveforms and biases\n- Low-noise analog signal processing and digitization\n- 1 second frame time for negligible pileup\n\n## XRS Instrument Control Unit (XICU)\n- Controls XRS activities and data transfer\n- Event Recognition Processor (ERP) extracts X-ray events\n- Reduces data rate by many orders of magnitude\n\n## CCD X-Ray Performance\n- Measured readout noise 2-3 e- RMS\n- Spectral resolution meets Arcus requirements\n- FWHM < 70 eV at 0.5 keV\n\n## CCID-94 Characteristics\n- Back-illuminated frame-transfer CCDs\n- 2048 × 1024 pixel imaging array\n- 24 × 24 µm image area pixel size\n- 50 µm detector thickness\n\n## Contamination Blocking Filter (CBF)\n- Protects detectors from molecular contamination\n- 45 nm polyimide + 30 nm Al\n- Maintained above +20°C by heater control\n\n## Optical Blocking Filter (OBF)\n- Attenuates visible/IR stray light\n- 40 nm Al on-chip filter\n- Works in conjunction with CBF"
                }
            }
        }
    if data['status'] != 'success':
        print('Failed to fetch data')
    else:
        for category, catdata in data.items():
            if category != 'status':
                for paper_id, paperdata in catdata.items():
                    title = paperdata['title']
                    category = paperdata['category']
                    summary = paperdata['summary']
                    mindmap = paperdata['mindmap']
                    citation = paperdata['citation']
                    access_key = access_key
                    status = post_blog(title, category, summary, mindmap, citation, access_key)
                    print(status)
                    return status
                
if __name__ == '__main__':
    test(access_key)