import gradio as gr
from huggingface_hub import list_bucket_tree, download_bucket_files, login
import pandas as pd
import json
import os
import tempfile
from dotenv import load_dotenv

load_dotenv()
BUCKET_NAME = os.getenv("BUCKET_NAME")
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN:
    login(token=HF_TOKEN)

PAGE_SIZE = 5
def extract_customer_data():
    try:
        if not BUCKET_NAME:
            print("❌ BUCKET_NAME missing")
            return pd.DataFrame()

        tree = list_bucket_tree(
            BUCKET_NAME,
            recursive=True,
            token=HF_TOKEN
        )
        json_files = [
            item.path for item in tree
            if item.path.endswith("cameras.json")
        ]

        if not json_files:
            return pd.DataFrame(columns=["customer_id", "customer_name", "customer_email"])
        tmp_dir = tempfile.gettempdir()
        files_map = [
            (f, os.path.join(tmp_dir, f.replace("/", "_")))
            for f in json_files
        ]
        download_bucket_files(
            BUCKET_NAME,
            files=files_map,
            token=HF_TOKEN
        )
        rows = []
        for remote_path, local_path in files_map:
            parts = remote_path.split("/")
            customer_id = parts[1] if len(parts) > 1 else "unknown"
            try:
                with open(local_path, "r", encoding="utf-8") as f:
                    data = json.load(f)
                for item in data:
                    rows.append({
                        "customer_id": customer_id,
                        "customer_name": item.get("customer_name"),
                        "customer_email": item.get("customer_email")
                    })
            except Exception as e:
                print(f"File error {remote_path}: {e}")
        df = pd.DataFrame(rows)
        if not df.empty:
            df = df.drop_duplicates()
        return df
    except Exception as e:
        print("❌ BUCKET ERROR:", e)
        return pd.DataFrame(columns=["customer_id", "customer_name", "customer_email"])
def safe_load():
    try:
        return extract_customer_data()
    except Exception as e:
        print("❌ SAFE LOAD ERROR:", e)
        return pd.DataFrame(columns=["customer_id", "customer_name", "customer_email"])
df_global = safe_load()
def apply_search(df, email_query):
    if df is None or df.empty:
        return df
    if email_query:
        df = df[df["customer_email"].astype(str).str.contains(email_query, case=False, na=False)]
    return df
def build_view(df, page, email_query):
    filtered = apply_search(df, email_query)
    total_pages = max(1, (len(filtered) - 1) // PAGE_SIZE + 1)
    page = max(0, min(page, total_pages - 1))
    start = page * PAGE_SIZE
    end = start + PAGE_SIZE
    paged = filtered.iloc[start:end]
    status = f"Page {page+1}/{total_pages} | Total: {len(filtered)}"
    return paged, page, status
with gr.Blocks() as app:
    gr.Markdown("# 🛰 Buck Tracker Customer Dashboard")
    state_df = gr.State(df_global)
    state_page = gr.State(0)
    search_box = gr.Textbox(label="🔍 Search Email")
    table = gr.Dataframe(
        value=build_view(df_global, 0, "")[0],
        interactive=False
    )
    status = gr.Textbox(interactive=False)
    with gr.Row():
        refresh_btn = gr.Button("🔄 Refresh")
        prev_btn = gr.Button("⬅️")
        next_btn = gr.Button("➡️")
    def refresh(email):
        df = safe_load()
        table, page, status_text = build_view(df, 0, email)
        return table, 0, status_text, df
    def next_page(df, page, email):
        return *build_view(df, page + 1, email), df
    def prev_page(df, page, email):
        return *build_view(df, page - 1, email), df
    search_box.change(
        fn=lambda df, q: (*build_view(df, 0, q), df),
        inputs=[state_df, search_box],
        outputs=[table, state_page, status, state_df]
    )
    refresh_btn.click(
        fn=refresh,
        inputs=[search_box],
        outputs=[table, state_page, status, state_df]
    )
    next_btn.click(
        fn=next_page,
        inputs=[state_df, state_page, search_box],
        outputs=[table, state_page, status, state_df]
    )
    prev_btn.click(
        fn=prev_page,
        inputs=[state_df, state_page, search_box],
        outputs=[table, state_page, status, state_df]
    )
app.queue()
app.launch()