"""FastAPI endpoint
To run locally use 'uvicorn app:app --host localhost --port 7860'
or
`python -m uvicorn app:app --reload --host localhost --port 7860`
"""
import datetime as dt
import json
import logging
import numpy as np
import os
import random
from typing import Dict, List

import uvicorn
from fastapi import FastAPI, HTTPException, Request, Response
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates

import scripts.sentiment as sentiment
import scripts.twitter_scraper as ts
import scripts.utils as utils
from scripts import generative
import nltk

logging.basicConfig(level=logging.INFO)

app = FastAPI()
templates = Jinja2Templates(directory="templates")
app.mount("/static", StaticFiles(directory="static"), name="static")

# Construct absolute path to models folder
models_path = os.path.abspath("models")

username_list = [
    "alikarimi_ak8",
    "elonmusk",
    "BarackObama",
    "taylorlorenz",
    "cathiedwood",
    "ylecun",
]

## Static objects/paths
start_date = dt.date(year=2023, month=2, day=1)
end_date = dt.date(year=2023, month=3, day=22)


@app.get("/", response_class=HTMLResponse)
async def webpage(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})


@app.get("/accounts")
def get_accounts() -> List[dict]:
    import pandas as pd

    logging.info(f"Pulling account information on {username_list}")
    account_info_list = [
        ts.get_twitter_account_info(twitter_handle=account) for account in username_list
    ]
    df_account = pd.DataFrame(account_info_list)
    df_account = df_account.style.bar(
        subset=["follower_count", "friends_count"], color="#d65f5f"
    )
    df_account = df_account.format(
        {"follower_count": "{:,.0f}", "friends_count": "{:,.0f}"}
    )
    return HTMLResponse(content=df_account.to_html(classes="center"), status_code=200)


@app.get("/tweets/{username}", response_model=dict)
def get_tweets(username: str) -> dict:
    if username in username_list:
        # query = f"from:{username} since:{start_date} until:{end_date}"
        return ts.get_tweets(query=query)
    else:
        return {"detail": "Account not in scope of project."}


@app.get("/audience/{username}", response_model=dict)
def get_audience(username: str) -> dict:
    if username in username_list:
        query = f"from:{username} since:{start_date} until:{end_date}"
        tweets = ts.get_tweets(query=query)

        n_samples = 5
        # Random sample 3 tweets from user
        tweets_sampled = random.sample(tweets, n_samples)

        # Get all replies to sampled tweets
        tweet_threads = []
        for tweet in tweets_sampled:
            threads = ts.get_replies(
                username=tweet["username"],
                conversation_id=tweet["conversation_id"],
                max_tweets=100,
            )
            tweet_threads += threads

        # Get usernames from sample threads tweets
        usernames = [t["username"] for t in tweet_threads]
        # Get user info from sample replies to sampled tweets of user
        info_accounts = [
            ts.get_twitter_account_info(twitter_handle=account) for account in usernames
        ]

        # "follower_count":1,"friends_count":20,"verified":false}
        # Get stats for followers/audience engaging with tweets
        follower_counts = [
            info_accounts[i]["follower_count"] for i in range(len(info_accounts))
        ]
        friends_counts = [
            info_accounts[i]["friends_count"] for i in range(len(info_accounts))
        ]
        verified_counts = [
            1 if info_accounts[i]["verified"] == True else 0
            for i in range(len(info_accounts))
        ]
        return {
            "sample_size": len(info_accounts),
            "mean_follower_count": round(np.mean(follower_counts), 3),
            "mean_friends_count": round(np.mean(friends_counts), 3),
            "mean_verified": round(np.mean(verified_counts), 3),
        }
    else:
        response = Response(content="Account not in scope of project.", status_code=404)
        return response


@app.get("/sentiment/{username}")
async def get_sentiment(username: str) -> Dict[str, Dict[str, float]]:
    if username not in username_list:
        raise HTTPException(status_code=404, detail="Account not in scope of project.")

    query = f"from:{username} since:{start_date} until:{end_date}"
    tweets = ts.get_tweets(query=query)
    n_samples = 5
    tweets_sampled = random.sample(tweets, n_samples)

    tweet_threads = []
    for tweet in tweets_sampled:
        threads = ts.get_replies(
            username=tweet["username"],
            conversation_id=tweet["conversation_id"],
            max_tweets=100,
        )
        tweet_threads += threads

    print(
        f"Total replies to {n_samples} sampled tweets from username: {username}, {len(tweet_threads)}"
    )

    ## Sentiment scoring
    print(f"Running tweet sentiment scoring on username: {username} tweets")
    tweets_scores = sentiment.get_tweets_sentiment(tweets=tweets)
    mean_tweets_score = round(np.mean(tweets_scores), 2)
    ci_tweets = utils.wilson_score_interval(tweets_scores)

    # Get sentiment of the threads from tweets
    # Get username tweets sentiment
    print(f"Running tweet thread sentiment scoring on username: {username} tweets")
    threads_scores = sentiment.get_tweets_sentiment(tweets=tweet_threads)
    mean_threads_score = round(np.mean(threads_scores), 2)
    ci_threads = utils.wilson_score_interval(threads_scores)

    return {
        "thread_level": {
            "mean": mean_threads_score,
            "confidence_interal": ci_threads,
        },
        "audience_level": {
            "mean": mean_tweets_score,
            "confidence_interval": ci_tweets,
        },
    }


@app.post("/api/generate")
async def generate_text(request: Request):
    print("*" * 50)
    data = await request.json()
    print("*" * 50)
    print("POST Request:")

    # Check length of input, if it is greater than 10 tokens, the text is sent off to a summarizer to generate:
    try:
        generated_text = generative.generate_account_text(
            prompt=data["text"], model_dir=os.path.join(models_path, data["account"])
        )
        logging.info("INFO: Successfully generate text from model.")
    except Exception as e:
        logging.error(f"Error generating text: {e}")
        return {"error": "Error generating text"}
    # return one example
    generated_text = generated_text[0]["generated_text"]

    ###################################################
    ## Clean up generate text
    # Get rid of final sentence
    # sentences = nltk.sent_tokenize(generated_text)
    # unique_sentences = set()
    # non_duplicate_sentences = []
    # for sentence in sentences:
    #     if sentence not in unique_sentences:
    #         non_duplicate_sentences.append(sentence)
    #         unique_sentences.add(sentence)
    # final_text = " ".join(non_duplicate_sentences[:-1])

    final_text= generated_text
    return {"generated_text": final_text}


@app.get("/examples1")
async def read_examples():
    with open("templates/charts/handle_sentiment_breakdown.html") as f:
        html = f.read()
    return HTMLResponse(content=html)


@app.get("/examples2")
async def read_examples():
    with open("templates/charts/handle_sentiment_timesteps.html") as f:
        html = f.read()
    return HTMLResponse(content=html)

# uvicorn --workers=2 app:app
# if __name__ == "__main__":
#     # uvicorn.run(app, host="0.0.0.0", port=8000)
#     uvicorn.run("app:app", host="127.0.0.1", port=5049, reload=True)