app.py

"""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 sys
import spacy

# sys.setrecursionlimit(20000)
import pandas as pd
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, JSONResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates

from rouge_score import rouge_scorer
# Scripts
import scripts.sentiment as sentiment
import scripts.twitter_scraper as ts
from scripts import sentiment
from scripts.summarization import bert_summarization
from scripts.twitter_scraper import get_latest_account_tweets
from scripts.sentiment import twitter_sentiment_api_score
from scripts import twitter_scraper as ts
import scripts.utils as utils
from scripts import translation
from scripts import generative
import nltk

logging.basicConfig(level=logging.INFO)
pd.set_option('display.max_colwidth', 20)

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)

# Load spacy module on app start
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("sentencizer")


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


@app.get("/accounts")
async 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}"}
    )
    html_table = df_account.to_html(classes="center", index=False)

    return HTMLResponse(content=html_table, status_code=200)


@app.get("/tweets/{username}")
def get_tweets_username(username: str) -> dict:
    # Method 2: Use Snscrape
    df_tweets = ts.get_tweets(handle=username)

    if isinstance(df_tweets, pd.DataFrame):
        df_tweets = df_tweets[["handle", "created_at","retweet_count","view_count","like_count", "full_text"]]
        df_tweets["created_at"] = df_tweets["created_at"].dt.strftime(
            "%Y-%m-%d %H:%M:%S"
        )
        df_tweets = df_tweets.sort_values("created_at", ascending=False)  

        # Additional processing
        logging.info("Running sentiment on tweets")
        sentiments = twitter_sentiment_api_score(
            df_tweets['full_text'].to_list(), use_api=False
        )
        df_tweets["sentiment"] = [s['argmax'] for s in sentiments]
        if username == "alikarimi_ak8": 
            p = translation.PersianTextProcessor()
            df_tweets['full_text_translated'] = df_tweets["full_text"].apply(lambda c: p.translate_text(persian_text = c))


        df_tweets_html = df_tweets.to_html(classes="center", index=False, escape=False)
        df_tweets.to_html(open("df_tweets_html.html", "w"))
        df_tweets_data = df_tweets.to_dict(orient="records")

        response_data = {"html": df_tweets_html, "data": df_tweets_data}

        return JSONResponse(content=response_data, status_code=200)
    else:
        print("Error: Failed to retrieve tweets.")
        return df_tweets


@app.get("/audience/{username}", response_model=dict)
async 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,
        },
    }


## APIs: Primarily called by the index page
@app.post("/api/generate")
async def generate_text(request: Request):
    """Generate text from a prompt.

    Args:
        request: The HTTP request.

    Returns:
        The generated text.
    """
    print("*" * 50)
    data = await request.json()
    print("*" * 50)
    logging.info("POST to api/generate received and processing")

    # 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])

    return {"generated_text": final_text}


@app.post("/api/generate_summary")
async def generate_summary(request: Request):
    """Generate summary from tweets

    Args:
        request: The HTTP request.

    Returns:
        The generated text.
    """

    print("*" * 50)
    data = await request.json()
    print("data", data["tweetsData"])
    # Get the list of text
    tweets = [t["full_text"] for t in data["tweetsData"]]

    # Concatenate tweets into a single string
    text = " .".join(tweets)

    sentences = nlp(text).sents

    sentences = list(sentences)

    # Option 2
    sampled_sentences = random.sample(sentences, int(0.1 * len(sentences)))

    sampled_sentences = [sentiment.tweet_cleaner(s.text) for s in sampled_sentences]

    # Join the strings into one text blob
    tweet_blob = " ".join(sampled_sentences)

    # Generate the summary
    summary = bert_summarization(tweet_blob)
    print("Summary:", summary)
    # Return the summary
    return {"tweets_summary": summary}


## Historical Tweets pages
@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=5050, reload=True)