import torch
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread
import random
from datasets import load_dataset
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
from typing import List, Tuple
import json
from datetime import datetime

# GPU 메모리 관리
torch.cuda.empty_cache()

# 환경 변수 설정
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = "CohereForAI/c4ai-command-r7b-12-2024"
MODELS = os.environ.get("MODELS")
MODEL_NAME = MODEL_ID.split("/")[-1]

# 모델과 토크나이저 로드
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# 위키피디아 데이터셋 로드
wiki_dataset = load_dataset("lcw99/wikipedia-korean-20240501-1million-qna")
print("Wikipedia dataset loaded:", wiki_dataset)

# TF-IDF 벡터라이저 초기화 및 학습
print("TF-IDF 벡터화 시작...")
questions = wiki_dataset['train']['question'][:10000]  # 처음 10000개만 사용
vectorizer = TfidfVectorizer(max_features=1000)
question_vectors = vectorizer.fit_transform(questions)
print("TF-IDF 벡터화 완료")

class ChatHistory:
    def __init__(self):
        self.history = []
        self.history_file = "/tmp/chat_history.json"
        self.load_history()

    def add_conversation(self, user_msg: str, assistant_msg: str):
        conversation = {
            "timestamp": datetime.now().isoformat(),
            "messages": [
                {"role": "user", "content": user_msg},
                {"role": "assistant", "content": assistant_msg}
            ]
        }
        self.history.append(conversation)
        self.save_history()

    def format_for_display(self):
        formatted = []
        for conv in self.history:
            formatted.append([
                conv["messages"][0]["content"],
                conv["messages"][1]["content"]
            ])
        return formatted

    def get_messages_for_api(self):
        messages = []
        for conv in self.history:
            messages.extend([
                {"role": "user", "content": conv["messages"][0]["content"]},
                {"role": "assistant", "content": conv["messages"][1]["content"]}
            ])
        return messages

    def clear_history(self):
        self.history = []
        self.save_history()

    def save_history(self):
        try:
            with open(self.history_file, 'w', encoding='utf-8') as f:
                json.dump(self.history, f, ensure_ascii=False, indent=2)
        except Exception as e:
            print(f"히스토리 저장 실패: {e}")

    def load_history(self):
        try:
            if os.path.exists(self.history_file):
                with open(self.history_file, 'r', encoding='utf-8') as f:
                    self.history = json.load(f)
        except Exception as e:
            print(f"히스토리 로드 실패: {e}")
            self.history = []

# 전역 ChatHistory 인스턴스 생성
chat_history = ChatHistory()

def find_relevant_context(query, top_k=3):
    # 쿼리 벡터화
    query_vector = vectorizer.transform([query])
    
    # 코사인 유사도 계산
    similarities = (query_vector * question_vectors.T).toarray()[0]
    
    # 가장 유사한 질문들의 인덱스
    top_indices = np.argsort(similarities)[-top_k:][::-1]
    
    # 관련 컨텍스트 추출
    relevant_contexts = []
    for idx in top_indices:
        if similarities[idx] > 0:
            relevant_contexts.append({
                'question': questions[idx],
                'answer': wiki_dataset['train']['answer'][idx],
                'similarity': similarities[idx]
            })
    
    return relevant_contexts

def analyze_file_content(content, file_type):
    """Analyze file content and return structural summary"""
    if file_type in ['parquet', 'csv']:
        try:
            lines = content.split('\n')
            header = lines[0]
            columns = header.count('|') - 1
            rows = len(lines) - 3
            return f"📊 데이터셋 구조: {columns}개 컬럼, {rows}개 데이터"
        except:
            return "❌ 데이터셋 구조 분석 실패"
    
    lines = content.split('\n')
    total_lines = len(lines)
    non_empty_lines = len([line for line in lines if line.strip()])
    
    if any(keyword in content.lower() for keyword in ['def ', 'class ', 'import ', 'function']):
        functions = len([line for line in lines if 'def ' in line])
        classes = len([line for line in lines if 'class ' in line])
        imports = len([line for line in lines if 'import ' in line or 'from ' in line])
        return f"💻 코드 구조: {total_lines}줄 (함수: {functions}, 클래스: {classes}, 임포트: {imports})"
    
    paragraphs = content.count('\n\n') + 1
    words = len(content.split())
    return f"📝 문서 구조: {total_lines}줄, {paragraphs}단락, 약 {words}단어"

def read_uploaded_file(file):
    if file is None:
        return "", ""
    try:
        file_ext = os.path.splitext(file.name)[1].lower()
        
        if file_ext == '.parquet':
            df = pd.read_parquet(file.name, engine='pyarrow')
            content = df.head(10).to_markdown(index=False)
            return content, "parquet"
        elif file_ext == '.csv':
            encodings = ['utf-8', 'cp949', 'euc-kr', 'latin1']
            for encoding in encodings:
                try:
                    df = pd.read_csv(file.name, encoding=encoding)
                    content = f"📊 데이터 미리보기:\n{df.head(10).to_markdown(index=False)}\n\n"
                    content += f"\n📈 데이터 정보:\n"
                    content += f"- 전체 행 수: {len(df)}\n"
                    content += f"- 전체 열 수: {len(df.columns)}\n"
                    content += f"- 컬럼 목록: {', '.join(df.columns)}\n"
                    content += f"\n📋 컬럼 데이터 타입:\n"
                    for col, dtype in df.dtypes.items():
                        content += f"- {col}: {dtype}\n"
                    null_counts = df.isnull().sum()
                    if null_counts.any():
                        content += f"\n⚠️ 결측치:\n"
                        for col, null_count in null_counts[null_counts > 0].items():
                            content += f"- {col}: {null_count}개 누락\n"
                    return content, "csv"
                except UnicodeDecodeError:
                    continue
            raise UnicodeDecodeError(f"❌ 지원되는 인코딩으로 파일을 읽을 수 없습니다 ({', '.join(encodings)})")
        else:
            encodings = ['utf-8', 'cp949', 'euc-kr', 'latin1']
            for encoding in encodings:
                try:
                    with open(file.name, 'r', encoding=encoding) as f:
                        content = f.read()
                    return content, "text"
                except UnicodeDecodeError:
                    continue
            raise UnicodeDecodeError(f"❌ 지원되는 인코딩으로 파일을 읽을 수 없습니다 ({', '.join(encodings)})")
    except Exception as e:
        return f"❌ 파일 읽기 오류: {str(e)}", "error"



CSS = """
/* 전체 페이지 스타일링 */
body {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    min-height: 100vh;
    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
/* 메인 컨테이너 */
.container {
    max-width: 1200px;
    margin: 0 auto;
    padding: 2rem;
    background: rgba(255, 255, 255, 0.95);
    border-radius: 20px;
    box-shadow: 0 20px 40px rgba(0, 0, 0, 0.1);
    backdrop-filter: blur(10px);
    transform: perspective(1000px) translateZ(0);
    transition: all 0.3s ease;
}
/* 제목 스타일링 */
h1 {
    color: #2d3436;
    font-size: 2.5rem;
    text-align: center;
    margin-bottom: 2rem;
    text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.1);
    transform: perspective(1000px) translateZ(20px);
}
h3 {
    text-align: center;
    color: #2d3436;
    font-size: 1.5rem;
    margin: 1rem 0;
}
/* 채팅박스 스타일링 */
.chatbox {
    background: white;
    border-radius: 15px;
    box-shadow: 0 8px 32px rgba(31, 38, 135, 0.15);
    backdrop-filter: blur(4px);
    border: 1px solid rgba(255, 255, 255, 0.18);
    padding: 1rem;
    margin: 1rem 0;
    transform: translateZ(0);
    transition: all 0.3s ease;
}
/* 메시지 스타일링 */
.chatbox .messages .message.user {
    background: linear-gradient(145deg, #e1f5fe, #bbdefb);
    border-radius: 15px;
    padding: 1rem;
    margin: 0.5rem;
    box-shadow: 5px 5px 15px rgba(0, 0, 0, 0.05);
    transform: translateZ(10px);
    animation: messageIn 0.3s ease-out;
}
.chatbox .messages .message.bot {
    background: linear-gradient(145deg, #f5f5f5, #eeeeee);
    border-radius: 15px;
    padding: 1rem;
    margin: 0.5rem;
    box-shadow: 5px 5px 15px rgba(0, 0, 0, 0.05);
    transform: translateZ(10px);
    animation: messageIn 0.3s ease-out;
}
/* 버튼 스타일링 */
.duplicate-button {
    background: linear-gradient(145deg, #24292e, #1a1e22) !important;
    color: white !important;
    border-radius: 100vh !important;
    padding: 0.8rem 1.5rem !important;
    box-shadow: 3px 3px 10px rgba(0, 0, 0, 0.2) !important;
    transition: all 0.3s ease !important;
    border: none !important;
    cursor: pointer !important;
}
.duplicate-button:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.3) !important;
}
/* 입력 필드 스타일링 */
"""

@spaces.GPU
def stream_chat(message: str, history: list, uploaded_file, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
    try:
        print(f'message is - {message}')
        print(f'history is - {history}')
        
        # 파일 업로드 처리
        file_context = ""
        if uploaded_file:
            content, file_type = read_uploaded_file(uploaded_file)
            if content:
                file_context = f"\n\n업로드된 파일 내용:\n```\n{content}\n```"

        # 관련 컨텍스트 찾기
        relevant_contexts = find_relevant_context(message)
        wiki_context = "\n\n관련 위키피디아 정보:\n"
        for ctx in relevant_contexts:
            wiki_context += f"Q: {ctx['question']}\nA: {ctx['answer']}\n유사도: {ctx['similarity']:.3f}\n\n"
        
        # 대화 히스토리 구성
        conversation = []
        for prompt, answer in history:
            conversation.extend([
                {"role": "user", "content": prompt},
                {"role": "assistant", "content": answer}
            ])

        # 최종 프롬프트 구성
        final_message = file_context + wiki_context + "\n현재 질문: " + message
        conversation.append({"role": "user", "content": final_message})

        input_ids = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
        inputs = tokenizer(input_ids, return_tensors="pt").to(0)

        streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)

        generate_kwargs = dict(
            inputs, 
            streamer=streamer,
            top_k=top_k,
            top_p=top_p,
            repetition_penalty=penalty,
            max_new_tokens=max_new_tokens, 
            do_sample=True, 
            temperature=temperature,
            eos_token_id=[255001],
        )
        
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()

        buffer = ""
        for new_text in streamer:
            buffer += new_text
            yield "", history + [[message, buffer]]

    except Exception as e:
        error_message = f"오류가 발생했습니다: {str(e)}"
        yield "", history + [[message, error_message]]

# UI 부분 수정
with gr.Blocks(css=CSS) as demo:
    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(
                value=[],
                height=500,
                label="대화창",
                show_label=True
            )
            
            msg = gr.Textbox(
                label="메시지 입력",
                show_label=False,
                placeholder="무엇이든 물어보세요... 💭",
                container=False
            )
            
            with gr.Row():
                clear = gr.ClearButton([msg, chatbot], value="대화내용 지우기")
                send = gr.Button("보내기 📤")
        
        with gr.Column(scale=1):
            gr.Markdown("### 파일 업로드 📁")
            file_upload = gr.File(
                label="파일 선택",
                file_types=["text", ".csv", ".parquet"],
                type="filepath"
            )
            
            with gr.Accordion("고급 설정 ⚙️", open=False):
                temperature = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.8, label="온도")
                max_new_tokens = gr.Slider(minimum=128, maximum=8000, step=1, value=4000, label="최대 토큰 수")
                top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.8, label="상위 확률")
                top_k = gr.Slider(minimum=1, maximum=20, step=1, value=20, label="상위 K")
                penalty = gr.Slider(minimum=0.0, maximum=2.0, step=0.1, value=1.0, label="반복 패널티")

    # 이벤트 바인딩
    msg.submit(
        stream_chat,
        inputs=[msg, chatbot, file_upload, temperature, max_new_tokens, top_p, top_k, penalty],
        outputs=[msg, chatbot]
    )

    send.click(
        stream_chat,
        inputs=[msg, chatbot, file_upload, temperature, max_new_tokens, top_p, top_k, penalty],
        outputs=[msg, chatbot]
    )

    def init_msg():
        return "파일 분석을 시작합니다..."

    # 파일 업로드시 자동 분석
    file_upload.change(
        init_msg,
        outputs=msg
    ).then(
        stream_chat,
        inputs=[msg, chatbot, file_upload, temperature, max_new_tokens, top_p, top_k, penalty],
        outputs=[msg, chatbot]
    )

if __name__ == "__main__":
    demo.launch()