import streamlit as st
import random
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

EMO_NAMES = {0: "amusement", 1: "anger", 2: "awe", 3: "contentment", 
             4: "disgust", 5: "excitement", 6: "fear", 7: "sadness"}

def render_dataset_tab(matcher, image_files, embeddings, labels_array, images_path):
    if image_files is None:
        st.error("Ошибка загрузки данных EmoSet. Проверьте пути.")
        return

    # Инициализация состояния именно для этой вкладки
    if 'ds_round' not in st.session_state:
        st.session_state.ds_round = 1
        st.session_state.ds_chosen_indices = []
        st.session_state.ds_current_options = random.sample(range(len(image_files)), 6)

    st.write("Выберите изображение, соответствующее вашему настроению:")

    if st.session_state.ds_round <= 10:
        st.subheader(f"Раунд {st.session_state.ds_round} из 10")
        
        cols = st.columns(3)
        for i, idx in enumerate(st.session_state.ds_current_options):
            with cols[i % 3]:
                img_name = image_files[idx]
                img = Image.open(images_path / img_name)
                st.image(img, use_container_width=True)
                
                if matcher:
                    v_p, a_p = matcher.predict_va(embeddings[idx])
                    gt_label = EMO_NAMES.get(labels_array[idx], "unknown")
                    st.caption(f"GT: {gt_label} | Pred: V:{v_p:.1f} A:{a_p:.1f}")
                
                if st.button(f"Выбрать образ {i+1}", key=f"btn_ds_{idx}", use_container_width=True):
                    st.session_state.ds_chosen_indices.append(idx)
                    st.session_state.ds_round += 1
                    if st.session_state.ds_round <= 10:
                        st.session_state.ds_current_options = random.sample(range(len(image_files)), 6)
                    st.rerun()
    else:
        st.success("✅ Анализ завершен! Ваш эмоциональный профиль готов.")
        
        all_v, all_a = [], []
        for idx in st.session_state.ds_chosen_indices:
            v, a = matcher.predict_va(embeddings[idx])
            all_v.append(v)
            all_a.append(a)
        
        target_v, target_a = np.mean(all_v), np.mean(all_a)
        playlist = matcher.find_nearest_tracks(target_v, target_a, top_k=5)

        col_left, col_right = st.columns([1, 2])

        with col_left:
            st.header("📊 Ваш профиль")
            st.metric("Позитивность (Valence)", f"{target_v:.2f}")
            st.metric("Энергия (Arousal)", f"{target_a:.2f}")
            
            fig, ax = plt.subplots(figsize=(4, 4))
            ax.set_xlim(1, 9); ax.set_ylim(1, 9)
            ax.axhline(5, color='gray', lw=1, ls='--'); ax.axvline(5, color='gray', lw=1, ls='--')
            ax.scatter(target_v, target_a, color='red', s=150, edgecolors='white', zorder=5)
            ax.set_xlabel("Valence"); ax.set_ylabel("Arousal")
            st.pyplot(fig)

        with col_right:
            st.header("🎵 Рекомендованная музыка")
            for _, row in playlist.iterrows():
                with st.container(border=True):
                    c1, c2 = st.columns([1, 3])
                    with c1:
                        st.write(f"**ID:** {int(row['song_id'])}")
                        score_val = row.get('final_score', row.get('emo_distance', 0))
                        st.caption(f"Dist Score: {score_val:.2f}")
                    with c2:
                        audio_path = matcher.get_audio_path(row['song_id'])
                        if audio_path:
                            st.audio(str(audio_path))
                        else:
                            st.warning(f"Файл {int(row['song_id'])}.mp3 не найден")

        if st.button("Начать заново", type="primary"):
            st.session_state.pop('ds_round', None)
            st.session_state.pop('ds_chosen_indices', None)
            st.session_state.pop('ds_current_options', None)
            st.rerun()