#Class to fetch news and stock data from the web for a specific ticker and combine them into a dataframe.

import pandas as pd
import yfinance as yf
from pygooglenews import GoogleNews
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
class InferenceDataPipeline:
    def __init__(self, ticker, time_period_news, time_period_stock):
        self.ticker = ticker
        self.time_period_news = time_period_news
        self.time_period_stock = time_period_stock

    def get_data(self):
        stock_data = self.get_stock_data()
        news_data = self.get_news_data()
        news_sentiment = self.get_sentiment(news_data)
        combined_data = self.combine_data(stock_data, news_sentiment)

        return combined_data


    def get_stock_data(self):
        data = yf.download(self.ticker , period = self.time_period_stock)
        df = pd.DataFrame()
        df['Open'] = data['Open']
        df['Close'] = data['Close']
        df['High'] = data['High']
        df['Low'] = data['Low']
        df['Volume'] = data['Volume']

        return df

    def get_news_data(self):
        googlenews = GoogleNews()
        news_data = googlenews.search(self.ticker, when=self.time_period_news)
        news_data = pd.DataFrame(news_data['entries'])
        return news_data

    def get_sentiment(self, news_data):
        tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
        model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")
        classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)

        news_sentiment = []
        for i in range(len(news_data)):
            sentiment = classifier(news_data['title'][i], top_k=None)
            postive_score = sentiment[0]['score']
            negative_score = sentiment[1]['score']
            neutral_score = sentiment[2]['score']
            reformmated_time_stamp = pd.to_datetime(news_data['published'][i]).date()
            news_sentiment.append({'Date': reformmated_time_stamp, 'positive_score': postive_score, 'negative_score': negative_score, 'neutral_score': neutral_score})
        return pd.DataFrame(news_sentiment)

    def combine_data(self, stock_data, news_sentiment):
        news_sentiment = (
            news_sentiment
            .groupby('Date')
            .mean()
            .fillna(0)
            .reset_index()
            .set_index('Date')
            .sort_index()
        )

        common_index = pd.date_range(
            start=pd.Timestamp(min(pd.Timestamp(stock_data.index[0]), pd.Timestamp(news_sentiment.index[0]))),
            end=pd.Timestamp(max(pd.Timestamp(stock_data.index[-1]), pd.Timestamp(news_sentiment.index[-1]))),
            freq='D'
        )
        stock_data = stock_data.reindex(common_index).fillna(-1)

        news_sentiment = news_sentiment.reindex(common_index).fillna(0)

        #Ensure stock_data and news_sentiment have combatile indices
        stock_data.index = pd.to_datetime(stock_data.index).normalize()
        news_sentiment.index = pd.to_datetime(news_sentiment.index).normalize()

        combined_data = pd.merge(
            stock_data,
            news_sentiment,
            how='left',
            left_index=True,
            right_index=True
        )

        #Drop all close values that are -1
        combined_data = combined_data[combined_data['Close'] != -1]
        #fill all missing values with 0
        combined_data = combined_data.fillna(0)

        return combined_data