import streamlit as st
import yfinance as yf
import pandas as pd
import cufflinks as cf
import datetime
import plotly.graph_objects as go

# App title
st.markdown('''
# Sovrenn Market Sentiment Indicator App
Shown are the stock price data for the selected company!

**Credits**
- App built by SRL
''')
st.write('---')

# Sidebar
st.sidebar.subheader('Query parameters')
start_date = st.sidebar.date_input("Start date", datetime.date(2023,9, 20))
#start_date = start_date - datetime.timedelta(days=1) 
end_date = start_date + datetime.timedelta(days=14)

# User input for the stock ticker symbol
tickerSymbol = st.sidebar.text_input('Enter Stock Ticker Symbol')

if tickerSymbol:
    tickerData = yf.Ticker(tickerSymbol)  # Get ticker data
    tickerDf = tickerData.history(period='1d', start=start_date, end=end_date)  # Get the historical prices for this ticker

    string_name = tickerData.info.get('longName', 'Company Name Not Available')
    st.header('**%s**' % string_name)

    # Try to get the business summary, handle KeyError if not available
    try:
        string_summary = tickerData.info['longBusinessSummary']
        st.info(string_summary)
    except KeyError:
        st.warning("Business summary not available for this company.")

    # Ticker data
    st.header('**Ticker data**')
    st.write(tickerDf)



    # Create a candlestick chart and volume bar chart
    fig_candlestick = go.Figure(data=[go.Candlestick(x=tickerDf.index,
                                                     open=tickerDf['Open'],
                                                     high=tickerDf['High'],
                                                     low=tickerDf['Low'],
                                                     close=tickerDf['Close'])])

    fig_volume = go.Figure(data=[go.Bar(x=tickerDf.index, y=tickerDf['Volume'])])

    st.header('**Candlestick Chart**')
    st.plotly_chart(fig_candlestick)

    st.header('**Volume Bar Chart**')
    st.plotly_chart(fig_volume)
    
    
    st.write(start_date)
    tickerDf = pd.DataFrame(tickerDf).reset_index()

#    st.write(tickerDf)

    #date = datetime.date(start_date)
    date_str = start_date.strftime("%Y-%m-%d")
    st.write(date_str)
    df = tickerDf[tickerDf["Date"]==date_str]
    st.write(df)

    if (df["Close"][0] > df["Open"][0] ):
        st.write("NSE has uptrend on " +date_str )
    if (df["Close"][0] < df["Open"][0] ):
        st.write(" NSE has downdtrend on " +date_str )




else:
    st.warning("Please enter a valid Stock Ticker Symbol.")



Begindatestring = start_date


#Begindatestring = datetime.strptime(Begindatestring, "%Y-%m-%d").date()


val = 39448 + int(delta.days)
url = 'https://economictimes.indiatimes.com/archivelist/year-'+str(Begindatestring.year)+',month-'+str(Begindatestring.month)+',starttime-'+str(val)+'.cms'  # Replace with your URL

response = requests.get(url)

if response.status_code == 200:
    html_text = response.text
    soup = BeautifulSoup(html_text, "lxml")
else:
    st.write(f"Failed to fetch the page. Status code: {response.status_code}")
jobs = soup.find_all("li")
headlines = []
for job in jobs:
        try:
            target_element = job.find("a")
            target_element.text
            headlines.append(target_element.text)
        except:
            continue




index = [idx for idx, s in enumerate(headlines) if s=='Most Read' ][0]
del headlines[index:]
news = pd.DataFrame({"News": headlines})
news.insert(0, 'Date', Begindatestring)
#st.dataframe(df[0:1])


news = news.drop_duplicates()
news = news.dropna(how='any')
news = news.reset_index(drop=True)

import pandas as pd
import numpy as np


from transformers import pipeline
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


tokenizer = AutoTokenizer.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")










nlp = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)

length = len(news[ 'News'].to_list())
news_list = news[ 'News'].to_list() 

df = pd.DataFrame()
for i in range (0, length):


    results = nlp(news_list[i])
    df.loc[i, "News"] = news_list[i]
    df.loc[i , 'label'] = results[0]["label"]
    df.loc[i , 'score'] = results[0]["score"]



#st.dataframe(df)

# Filter the DataFrame to get rows with "neutral" sentiment
bullish_rows = df[df['label'] == 'bullish']

# Calculate the sum of the 'Score' column for "neutral" rows
bullish_score_sum = bullish_rows['score'].sum()

num_bullish_rows = len(bullish_rows)
# Calculate the average score for "neutral" sentiment
average_score_for_bullish = bullish_score_sum / num_bullish_rows


# Filter the DataFrame to get rows with "neutral" sentiment
bearish_rows = df[df['label'] == 'bearish']

# Calculate the sum of the 'Score' column for "neutral" rows
bearish_score_sum = bearish_rows['score'].sum()

# Cabearishlculate the number of "neutral" rows
num_bearish_rows = len(bearish_rows)

# Calculate the average score for "neutral" sentiment
average_score_for_bearish = bearish_score_sum / num_bearish_rows


if(average_score_for_bearish > average_score_for_bullish):
     st.write("Stock will go down")
if(average_score_for_bearish < average_score_for_bullish):
     st.write("Stock will go up")