import os
import time
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import base64
from io import BytesIO
import requests

from core.dashboard import ErohaDashboard
from core.intelligence import update_memory, summarize_context
from core.selfcheck import evaluate_answer, improve_answer
from core.learning import analyze_user_input, adapt_answer
from core.model_selector import choose_model

# ==============================
# 🔐 ENV
# ==============================
HF_TOKEN = os.getenv("HF_TOKEN", "")
LOG_FILE = "logs/history.csv"
os.makedirs("logs", exist_ok=True)
dashboard = ErohaDashboard()

# ==============================
# 🔁 Автоматический Router с Failover
# ==============================
def query_huggingface_router(prompt, model_id):
    """
    Универсальный запрос к Router API с fallback на прямую модель.
    """
    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
    payload = {
        "inputs": prompt,
        "parameters": {"max_new_tokens": 600, "temperature": 0.7},
    }

    try:
        # 1️⃣ Основной Router API
        response = requests.post(
            "https://router.huggingface.co/models/" + model_id,
            headers=headers,
            json=payload,
            timeout=30,
        )
        if response.status_code == 200:
            return response.json()[0]["generated_text"]
        elif response.status_code == 410:
            raise RuntimeError("Old endpoint rejected (410)")
        else:
            raise RuntimeError(f"Router error: {response.status_code}")

    except Exception as e:
        print("⚠️ Router недоступен, выполняется fallback:", str(e))
        # 2️⃣ Fallback: прямое обращение к конкретной модели
        fallback_url = f"https://huggingface.co/api/models/{model_id}"
        try:
            response = requests.post(fallback_url, headers=headers, json=payload, timeout=30)
            if response.status_code == 200:
                return response.json()[0]["generated_text"]
            else:
                raise RuntimeError(f"Fallback error: {response.status_code}")
        except Exception as e2:
            return f"❌ Ошибка при обращении к API: {str(e2)}"

# ==============================
# 🧠 Основная логика
# ==============================
def generate_response(user_input):
    try:
        start = time.time()
        prefs = analyze_user_input(user_input)
        model_id = choose_model(user_input)

        result = query_huggingface_router(user_input, model_id)

        check = evaluate_answer(result)
        improved = improve_answer(result)
        personalized = adapt_answer(improved)
        update_memory(user_input, personalized)
        context = summarize_context()

        response_time = round(time.time() - start, 2)
        dashboard.log_request(model_id, prefs["category"], response_time)

        df = pd.DataFrame([{
            "time": time.strftime("%Y-%m-%d %H:%M:%S"),
            "model": model_id,
            "category": prefs["category"],
            "response_time": response_time,
            "prompt": user_input,
            "response": personalized[:1500],
        }])
        df.to_csv(LOG_FILE, mode="a", index=False, header=not os.path.exists(LOG_FILE))

        summary = (
            f"🧠 **Модель:** `{model_id}`\n"
            f"⚡ **Время отклика:** {response_time} сек\n"
            f"🔍 **Проверка качества:** {check['result']}\n"
            f"{'; '.join(check['feedback']) if check['feedback'] else '✅ Всё отлично'}\n\n"
            f"{context}"
        )

        return f"{personalized}\n\n{summary}"

    except Exception as e:
        return f"❌ Ошибка выполнения: {str(e)}"

# ==============================
# 📈 Визуализация аналитики
# ==============================
def generate_chart():
    if not os.path.exists(LOG_FILE):
        return "⚠️ Нет данных для графика."
    df = pd.read_csv(LOG_FILE)
    if df.empty:
        return "⚠️ История пуста."
    df["time"] = pd.to_datetime(df["time"])
    df = df.tail(50)

    plt.figure(figsize=(8, 4))
    plt.plot(df["time"], df["response_time"], marker="o", linewidth=1.5)
    plt.title("⚡ Скорость отклика моделей (последние 50 запросов)")
    plt.xlabel("Время")
    plt.ylabel("Секунды")
    plt.grid(True)
    buffer = BytesIO()
    plt.savefig(buffer, format="png", bbox_inches="tight")
    buffer.seek(0)
    img_base64 = base64.b64encode(buffer.read()).decode("utf-8")
    plt.close()
    return f"<img src='data:image/png;base64,{img_base64}'/>"

def show_dashboard():
    metrics_text, df = dashboard.dashboard_ui()
    chart_html = generate_chart()
    return metrics_text, df, chart_html

# ==============================
# 🎨 Интерфейс Gradio
# ==============================
with gr.Blocks(title="Eroha AgentAPI v5.6 — AutoRouter Failover Core", theme="soft") as app:
    gr.Markdown("# 🤖 Eroha AgentAPI v5.6 — Guru Edition (AutoRouter + Failover Core)")
    gr.Markdown("**Автоматический Router + fallback + визуальная аналитика** ⚙️")

    with gr.Tab("💬 Agent Chat"):
        user_input = gr.Textbox(label="Введите запрос", placeholder="Например: придумай философскую сказку об ИИ.")
        output_box = gr.Textbox(label="Ответ", lines=15)
        submit_btn = gr.Button("🚀 Отправить")
        submit_btn.click(fn=generate_response, inputs=user_input, outputs=output_box)

    with gr.Tab("📊 Dashboard"):
        metrics = gr.Markdown(label="📈 Общая статистика")
        log_table = gr.Dataframe(headers=["time", "model", "category", "response_time"], label="История запросов")
        chart_box = gr.HTML()
        refresh = gr.Button("🔄 Обновить дашборд")
        refresh.click(show_dashboard, outputs=[metrics, log_table, chart_box])

app.launch(server_name="0.0.0.0", server_port=7860)