import json
from typing import Any, Dict, List

import gradio as gr

from analysis_core import extract_chats, get_chat_name, analyze_chat


THEME = gr.themes.Soft(
    primary_hue="fuchsia",
    secondary_hue="pink",
    neutral_hue="slate",
)

CSS = """
/* full width */
.gradio-container {
  max-width: 100% !important;
  padding-left: 24px !important;
  padding-right: 24px !important;
}

/* buttons */
.btn-load button {
  background: linear-gradient(90deg, #d946ef, #ec4899) !important;
  border-radius: 16px !important;
  font-weight: 700 !important;
}
.btn-analyze button {
  background: linear-gradient(90deg, #22c55e, #16a34a) !important;
  border-radius: 16px !important;
  font-weight: 700 !important;
}

/* status */
.status-box textarea {
  border-radius: 14px !important;
  font-weight: 500;
}

/* plot */
.plot-container { min-height: 520px; }

/* -------- Dataframe styling (match magenta theme) -------- */
.lex-table .wrap {
  border-radius: 16px !important;
  border: 1px solid rgba(236,72,153,0.28) !important;
  overflow: hidden !important;
}

.lex-table table {
  border-collapse: separate !important;
  border-spacing: 0 !important;
}

/* header */
.lex-table thead th {
  background: linear-gradient(90deg, rgba(217,70,239,0.35), rgba(236,72,153,0.25)) !important;
  color: rgba(255,255,255,0.92) !important;
  font-weight: 800 !important;
  border-bottom: 1px solid rgba(236,72,153,0.28) !important;
}

/* body cells */
.lex-table tbody td {
  background: rgba(255,255,255,0.02) !important;
  border-bottom: 1px solid rgba(236,72,153,0.10) !important;
}

/* zebra rows */
.lex-table tbody tr:nth-child(even) td {
  background: rgba(217,70,239,0.06) !important;
}

/* hover */
.lex-table tbody tr:hover td {
  background: rgba(236,72,153,0.14) !important;
}

/* align */
.lex-table td, .lex-table th { padding: 10px 12px !important; }

/* make numbers a bit clearer */
.lex-table td:last-child {
  font-variant-numeric: tabular-nums;
}
"""


def _path(file_obj) -> str:
    if file_obj is None:
        raise gr.Error("upload Telegram result.json first.")
    if isinstance(file_obj, str):
        return file_obj
    if isinstance(file_obj, dict) and "path" in file_obj:
        return file_obj["path"]
    if hasattr(file_obj, "name") and isinstance(file_obj.name, str):
        return file_obj.name
    raise gr.Error("could not read uploaded file path.")


def lex_list_to_rows(lst: List[Dict[str, Any]]):
    if not lst:
        return []
    return [[d["word"], round(float(d["score"]), 6)] for d in lst]


def load_chats(file_obj):
    p = _path(file_obj)
    with open(p, "r", encoding="utf-8") as f:
        data = json.load(f)

    chats = extract_chats(data)
    labels = [f"{i} | {get_chat_name(c, f'Chat {i}')}" for i, c in enumerate(chats)]
    if not labels:
        raise gr.Error("no chats found. make sure you uploaded Telegram export result.json")

    state = {"data": data}
    return gr.update(choices=labels, value=labels[0]), state, f"loaded {len(labels)} chats."


def run_analysis(choice: str, state: Dict[str, Any], max_bert_persian: int):
    if not state or "data" not in state:
        raise gr.Error("Upload result.json and load chats first.")
    if not choice:
        raise gr.Error("Choose a chat first.")

    chats = extract_chats(state["data"])
    idx = int(choice.split("|", 1)[0].strip())
    if idx < 0 or idx >= len(chats):
        raise gr.Error("Invalid chat selection.")

    result, fig, pos_top, neg_top = analyze_chat(
        chats[idx],
        max_bert_persian=int(max_bert_persian),
    )

    pos_rows = lex_list_to_rows(pos_top) if pos_top else []
    neg_rows = lex_list_to_rows(neg_top) if neg_top else []

    return result, fig, pos_rows, neg_rows


with gr.Blocks(
    title="Telegram Sentiment Analysis",
    theme=THEME,
    css=CSS,
) as demo:

    gr.Markdown(
        "upload Telegram result.json → load chats → choose chat → analyze. "
        "weekly plot includes peak/low word annotations. tables show top lex words."
    )

    state = gr.State({})

    status = gr.Textbox(
        label="Status",
        interactive=False,
        elem_classes=["status-box"],
    )

    file_in = gr.File(
        label="Upload Telegram result.json",
        file_types=[".json"],
    )

    chat_dd = gr.Dropdown(
        label="Choose chat",
        choices=[],
        value=None,
    )

    max_bert = gr.Slider(
        minimum=0,
        maximum=2000,
        value=300,
        step=50,
        label="Max Persian messages to run BERT on (speed control)",
    )

    load_btn = gr.Button("Load chats", elem_classes=["btn-load"])
    analyze_btn = gr.Button("Analyze selected chat", elem_classes=["btn-analyze"])

    out_json = gr.JSON(label="Results (JSON)")

    out_plot = gr.Plot(
        label="Weekly emotion trajectory (with peak/low word annotations)",
        elem_classes=["plot-container"],
    )

    out_pos = gr.Dataframe(
        label="Top 5 positive lex words (word, score)",
        headers=["word", "score"],
        elem_classes=["lex-table"],
    )

    out_neg = gr.Dataframe(
        label="Top 5 negative lex words (word, score)",
        headers=["word", "score"],
        elem_classes=["lex-table"],
    )

    load_btn.click(load_chats, inputs=[file_in], outputs=[chat_dd, state, status])
    analyze_btn.click(
        run_analysis,
        inputs=[chat_dd, state, max_bert],
        outputs=[out_json, out_plot, out_pos, out_neg],
    )


demo.launch()