from fastapi import FastAPI, HTTPException, Depends, Security, BackgroundTasks
from fastapi.security import APIKeyHeader
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field
from typing import Literal, List, Dict
import os
from functools import lru_cache
from openai import OpenAI
from uuid import uuid4
import tiktoken
import sqlite3
import time
from datetime import datetime, timedelta
import asyncio
import requests
from prompts import CODING_ASSISTANT_PROMPT, NEWS_ASSISTANT_PROMPT, generate_news_prompt, SEARCH_ASSISTANT_PROMPT, generate_search_prompt
from fastapi_cache import FastAPICache
from fastapi_cache.backends.inmemory import InMemoryBackend
from fastapi_cache.decorator import cache

app = FastAPI()

API_KEY_NAME = "X-API-Key"
API_KEY = os.environ.get("CHAT_AUTH_KEY", "default_secret_key")
api_key_header = APIKeyHeader(name=API_KEY_NAME, auto_error=False)

ModelID = Literal[
    "meta-llama/llama-3-70b-instruct",
    "anthropic/claude-3.5-sonnet",
    "deepseek/deepseek-coder",
    "anthropic/claude-3-haiku",
    "openai/gpt-3.5-turbo-instruct",
    "qwen/qwen-72b-chat",
    "google/gemma-2-27b-it"
]

class QueryModel(BaseModel):
    user_query: str = Field(..., description="User's coding query")
    model_id: ModelID = Field(
        default="meta-llama/llama-3-70b-instruct",
        description="ID of the model to use for response generation"
    )
    conversation_id: str = Field(default_factory=lambda: str(uuid4()), description="Unique identifier for the conversation")
    user_id: str = Field(..., description="Unique identifier for the user")

    class Config:
        schema_extra = {
            "example": {
                "user_query": "How do I implement a binary search in Python?",
                "model_id": "meta-llama/llama-3-70b-instruct",
                "conversation_id": "123e4567-e89b-12d3-a456-426614174000",
                "user_id": "user123"
            }
        }

class NewsQueryModel(BaseModel):
    query: str = Field(..., description="News topic to search for")
    model_id: ModelID = Field(
        default="meta-llama/llama-3-70b-instruct",
        description="ID of the model to use for response generation"
    )
    class Config:
        schema_extra = {
            "example": {
                "query": "Latest developments in AI",
                "model_id": "meta-llama/llama-3-70b-instruct"
            }
        }

@lru_cache()
def get_api_keys():
    return {
        "OPENROUTER_API_KEY": f"sk-or-v1-{os.environ['OPENROUTER_API_KEY']}",
        "BRAVE_API_KEY": os.environ['BRAVE_API_KEY']
    }

api_keys = get_api_keys()
or_client = OpenAI(api_key=api_keys["OPENROUTER_API_KEY"], base_url="https://openrouter.ai/api/v1")

# In-memory storage for conversations
conversations: Dict[str, List[Dict[str, str]]] = {}
last_activity: Dict[str, float] = {}

# Token encoding
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")

def limit_tokens(input_string, token_limit=6000):
    return encoding.decode(encoding.encode(input_string)[:token_limit])

def calculate_tokens(msgs):
    return sum(len(encoding.encode(str(m))) for m in msgs)

def chat_with_llama_stream(messages, model="gpt-3.5-turbo", max_llm_history=4, max_output_tokens=2500):
    while calculate_tokens(messages) > (8000 - max_output_tokens):
        if len(messages) > max_llm_history:
            messages = [messages[0]] + messages[-max_llm_history:]
        else:
            max_llm_history -= 1
            if max_llm_history < 2:
                error_message = "Token limit exceeded. Please shorten your input or start a new conversation."
                raise HTTPException(status_code=400, detail=error_message)

    try:
        response = or_client.chat.completions.create(
            model=model,
            messages=messages,
            max_tokens=max_output_tokens,
            stream=True
        )
        
        full_response = ""
        for chunk in response:
            if chunk.choices[0].delta.content is not None:
                content = chunk.choices[0].delta.content
                full_response += content
                yield content
        
        # After streaming, add the full response to the conversation history
        messages.append({"role": "assistant", "content": full_response})
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error in model response: {str(e)}")

async def verify_api_key(api_key: str = Security(api_key_header)):
    if api_key != API_KEY:
        raise HTTPException(status_code=403, detail="Could not validate credentials")
    return api_key

# SQLite setup
DB_PATH = '/app/data/conversations.db'

def init_db():
    os.makedirs(os.path.dirname(DB_PATH), exist_ok=True)
    conn = sqlite3.connect(DB_PATH)
    c = conn.cursor()
    c.execute('''CREATE TABLE IF NOT EXISTS conversations
                 (id INTEGER PRIMARY KEY AUTOINCREMENT,
                  user_id TEXT,
                  conversation_id TEXT,
                  message TEXT,
                  response TEXT,
                  timestamp DATETIME DEFAULT CURRENT_TIMESTAMP)''')
    conn.commit()
    conn.close()

init_db()

def update_db(user_id, conversation_id, message, response):
    conn = sqlite3.connect(DB_PATH)
    c = conn.cursor()
    c.execute('''INSERT INTO conversations (user_id, conversation_id, message, response)
                 VALUES (?, ?, ?, ?)''', (user_id, conversation_id, message, response))
    conn.commit()
    conn.close()

async def clear_inactive_conversations():
    while True:
        current_time = time.time()
        inactive_convos = [conv_id for conv_id, last_time in last_activity.items() 
                           if current_time - last_time > 1800]  # 30 minutes
        for conv_id in inactive_convos:
            if conv_id in conversations:
                del conversations[conv_id]
            if conv_id in last_activity:
                del last_activity[conv_id]
        await asyncio.sleep(60)  # Check every minute

@app.on_event("startup")
async def startup_event():
    FastAPICache.init(InMemoryBackend(), prefix="fastapi-cache")
    asyncio.create_task(clear_inactive_conversations())

@app.post("/coding-assistant")
async def coding_assistant(query: QueryModel, background_tasks: BackgroundTasks, api_key: str = Depends(verify_api_key)):
    """
    Coding assistant endpoint that provides programming help based on user queries.
    Available models:
    - meta-llama/llama-3-70b-instruct (default)
    - anthropic/claude-3.5-sonnet
    - deepseek/deepseek-coder
    - anthropic/claude-3-haiku
    - openai/gpt-3.5-turbo-instruct
    - qwen/qwen-72b-chat
    - google/gemma-2-27b-it
    Requires API Key authentication via X-API-Key header.
    """
    if query.conversation_id not in conversations:
        conversations[query.conversation_id] = [
            {"role": "system", "content": "You are a helpful assistant proficient in coding tasks. Help the user in understanding and writing code."}
        ]
    
    conversations[query.conversation_id].append({"role": "user", "content": query.user_query})
    last_activity[query.conversation_id] = time.time()
    
    # Limit tokens in the conversation history
    limited_conversation = conversations[query.conversation_id]

    def process_response():
        full_response = ""
        for content in chat_with_llama_stream(limited_conversation, model=query.model_id):
            full_response += content
            yield content
        background_tasks.add_task(update_db, query.user_id, query.conversation_id, query.user_query, full_response)

    return StreamingResponse(process_response(), media_type="text/event-stream")

# New functions for news assistant

def internet_search(query, type = "web", num_results=20):
    if type == "web":
        url = "https://api.search.brave.com/res/v1/web/search"
    else:
        url = "https://api.search.brave.com/res/v1/news/search"

    headers = {
        "Accept": "application/json",
        "Accept-Encoding": "gzip",
        "X-Subscription-Token": api_keys["BRAVE_API_KEY"]
    }
    params = {"q": query}

    response = requests.get(url, headers=headers, params=params)

    if response.status_code != 200:
        return []
    
    if type == "web":
        search_data = response.json()["web"]["results"]
    else:
        search_data = response.json()["results"]
    processed_results = []

    for item in search_data:
        if not item.get("extra_snippets"):
            continue
        
        result = {
            "title": item["title"],
            "snippet": item["extra_snippets"][0],
            "last_updated": item.get("age", "")
        }
        processed_results.append(result)

    return processed_results[:num_results]

@lru_cache(maxsize=100)
def cached_internet_search(query: str):
    return internet_search(query, type = "news")


def analyze_news(query):
    news_data = cached_internet_search(query)
    
    if not news_data:
        return "Failed to fetch news data.", []

    # Prepare the prompt for the AI
    # Use the imported function to generate the prompt (now includes today's date)
    prompt = generate_news_prompt(query, news_data)

    messages = [
        {"role": "system", "content": NEWS_ASSISTANT_PROMPT},
        {"role": "user", "content": prompt}
    ]

    return messages

@app.post("/news-assistant")
async def news_assistant(query: NewsQueryModel, api_key: str = Depends(verify_api_key)):
    """
    News assistant endpoint that provides summaries and analysis of recent news based on user queries.
    Requires API Key authentication via X-API-Key header.
    """
    messages = analyze_news(query.query)
    
    if not messages:
        raise HTTPException(status_code=500, detail="Failed to fetch news data")
        
    def process_response():
        for content in chat_with_llama_stream(messages, model=query.model_id):
            yield content
#meta-llama/llama-3-70b-instruct  google/gemini-pro-1.5
    return StreamingResponse(process_response(), media_type="text/event-stream")

class SearchQueryModel(BaseModel):
    query: str = Field(..., description="Search query")
    model_id: ModelID = Field(
        default="meta-llama/llama-3-70b-instruct",
        description="ID of the model to use for response generation"
    )
    class Config:
        schema_extra = {
            "example": {
                "query": "What are the latest advancements in quantum computing?",
                "model_id": "meta-llama/llama-3-70b-instruct"
            }
        }

def analyze_search_results(query):
    search_data = internet_search(query, type="web")
    
    if not search_data:
        return "Failed to fetch search data.", []

    # Prepare the prompt for the AI
    prompt = generate_search_prompt(query, search_data)

    messages = [
        {"role": "system", "content": SEARCH_ASSISTANT_PROMPT},
        {"role": "user", "content": prompt}
    ]

    return messages

@app.post("/search-assistant")
async def search_assistant(query: SearchQueryModel, api_key: str = Depends(verify_api_key)):
    """
    Search assistant endpoint that provides summaries and analysis of web search results based on user queries.
    Requires API Key authentication via X-API-Key header.
    """
    messages = analyze_search_results(query.query)
    
    if not messages:
        raise HTTPException(status_code=500, detail="Failed to fetch search data")
        
    def process_response():
        for content in chat_with_llama_stream(messages, model=query.model_id):
            yield content

    return StreamingResponse(process_response(), media_type="text/event-stream")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)