from typing import List, Dict
import httpx
import gradio as gr
import pandas as pd
from huggingface_hub import HfApi, ModelCard
import base64
import io
import zipfile
import asyncio
import aiohttp
from pathlib import Path
import emoji

def search_hub(query: str, search_type: str) -> pd.DataFrame:
    api = HfApi()
    if search_type == "Models":
        results = api.list_models(search=query)
        data = [{"id": model.modelId, "author": model.author, "downloads": model.downloads, "link": f"https://huggingface.co/{model.modelId}"} for model in results]
    elif search_type == "Datasets":
        results = api.list_datasets(search=query)
        data = [{"id": dataset.id, "author": dataset.author, "downloads": dataset.downloads, "link": f"https://huggingface.co/datasets/{dataset.id}"} for dataset in results]
    elif search_type == "Spaces":
        results = api.list_spaces(search=query)
        data = [{"id": space.id, "author": space.author, "link": f"https://huggingface.co/spaces/{space.id}"} for space in results]
    else:
        data = []
    
    for i, item in enumerate(data, 1):
        item['number'] = i
        item['formatted_link'] = format_link(item, i, search_type)
    
    return pd.DataFrame(data)

def format_link(item: Dict, number: int, search_type: str) -> str:
    link = item['link']
    readme_link = f"{link}/blob/main/README.md"
    title = f"{number}. {item['id']}"
    
    metadata = f"Author: {item['author']}"
    if 'downloads' in item:
        metadata += f", Downloads: {item['downloads']}"
    
    html = f"""
    <div style="margin-bottom: 10px;">
        <strong>{title}</strong><br>
        <a href="{link}" target="_blank" style="color: #4a90e2; text-decoration: none;">View {search_type[:-1]}</a> | 
        <a href="{readme_link}" target="_blank" style="color: #4a90e2; text-decoration: none;">View README</a><br>
        <small>{metadata}</small>
    </div>
    """
    return html

async def download_readme(session: aiohttp.ClientSession, item: Dict) -> tuple[str, str]:
    """Download README.md file for a given item."""
    item_id = item['id']
    raw_url = f"https://huggingface.co/{item_id}/raw/main/README.md"
    try:
        async with session.get(raw_url) as response:
            if response.status == 200:
                content = await response.text()
                return item_id.replace('/', '_'), content
            return item_id.replace('/', '_'), f"# Error downloading README for {item_id}\nStatus code: {response.status}"
    except Exception as e:
        return item_id.replace('/', '_'), f"# Error downloading README for {item_id}\nError: {str(e)}"

async def download_all_readmes(data: List[Dict]) -> tuple[str, str]:
    """Download all README files and create a zip archive."""
    if not data:
        return "", "No results to download"
        
    zip_buffer = io.BytesIO()
    status_message = "Downloading READMEs..."
    
    async with aiohttp.ClientSession() as session:
        tasks = [download_readme(session, item) for item in data]
        results = await asyncio.gather(*tasks)
        
        with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zip_file:
            for filename, content in results:
                zip_file.writestr(f"{filename}.md", content)
    
    zip_buffer.seek(0)
    base64_zip = base64.b64encode(zip_buffer.getvalue()).decode()
    
    download_link = f"""
    <div style="margin-top: 10px;">
        <a href="data:application/zip;base64,{base64_zip}" 
           download="readmes.zip" 
           style="display: inline-block; padding: 10px 20px; 
                  background-color: #4CAF50; color: white; 
                  text-decoration: none; border-radius: 5px;">
            📥 Download READMEs Archive
        </a>
    </div>
    """
    
    return download_link, "READMEs ready for download!"

def display_results(df: pd.DataFrame):
    if df is not None and not df.empty:
        html = "<div style='max-height: 400px; overflow-y: auto;'>"
        for _, row in df.iterrows():
            html += row['formatted_link']
        html += "</div>"
        return html
    else:
        return "<p>No results found.</p>"

def SwarmyTime(data: List[Dict]) -> Dict:
    """Aggregates all content from the given data."""
    aggregated = {
        "total_items": len(data),
        "unique_authors": set(),
        "total_downloads": 0,
        "item_types": {"Models": 0, "Datasets": 0, "Spaces": 0}
    }

    for item in data:
        aggregated["unique_authors"].add(item.get("author", "Unknown"))
        aggregated["total_downloads"] += item.get("downloads", 0)
        
        if "modelId" in item:
            aggregated["item_types"]["Models"] += 1
        elif "dataset" in item.get("id", ""):
            aggregated["item_types"]["Datasets"] += 1
        else:
            aggregated["item_types"]["Spaces"] += 1

    aggregated["unique_authors"] = len(aggregated["unique_authors"])
    return aggregated

with gr.Blocks() as demo:
    gr.Markdown("## Search the Hugging Face Hub")
    with gr.Row():
        search_query = gr.Textbox(label="Search Query", value="awacke1")
        search_type = gr.Radio(["Models", "Datasets", "Spaces"], label="Search Type", value="Models")
        search_button = gr.Button("Search")
    
    results_html = gr.HTML(label="Search Results")
    download_button = gr.Button("📚 Download All READMEs", visible=False)
    download_status = gr.Markdown("", label="Download Status")
    download_area = gr.HTML("", label="Download Link")
    metadata_output = gr.Textbox(label="Metadata", lines=10)
    aggregated_output = gr.JSON(label="Aggregated Content")
    
    current_results = gr.State([])

    def search_and_aggregate(query, search_type):
        df = search_hub(query, search_type)
        data = df.to_dict('records')
        aggregated = SwarmyTime(data)
        html_results = display_results(df)
        show_download = len(data) > 0
        return [
            html_results,  # results_html
            show_download,  # download_button visible
            "",  # download_status
            "",  # download_area
            aggregated,  # aggregated_output
            data  # current_results
        ]

    async def handle_download(data):
        if not data:
            return ["No results to download", ""]
        download_link, status = await download_all_readmes(data)
        return [status, download_link]

    search_button.click(
        search_and_aggregate,
        inputs=[search_query, search_type],
        outputs=[
            results_html,
            download_button,
            download_status,
            download_area,
            aggregated_output,
            current_results
        ]
    )

    download_button.click(
        handle_download,
        inputs=[current_results],
        outputs=[download_status, download_area]
    )

demo.launch(debug=True)