app.py

import json
import streamlit as st
import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
import requests

 
url_stocks = "https://financialmodelingprep.com/api/v3/stock/list?apikey="
url_sentiment = "https://yaakovy-fin-proj-docker.hf.space/ticker/"
url_timeGpt =  "https://ofirmatzlawi-fin-proj-docker-1.hf.space/ticker/"
url_forecast_eod = "https://yaakovy-lasthourforcast.hf.space/ticker/"  
url_forecast_eod24 = "https://ofirmatzlawi-fin-proj-docker-2.hf.space/ticker/"
 
def get_max_sentiment(row):
    if row["sentiment_score"] > 0.05:  # Threshold for positive sentiment
        return "positive"
    elif row["sentiment_score"] < -0.05:  # Threshold for negative sentiment
        return "negative"
    else:
        return "neutral"
 

def get_sentiment_data(stock_info):
    symbol = stock_info.info['symbol']
    url_sentiment_with_ticker = f"{url_sentiment}{symbol}"
    response = requests.get(url_sentiment_with_ticker)
    if response.status_code == 200:
        json_data = json.loads(response.json())         
        df = pd.DataFrame(json_data)    
        df['sentiment'] = df.apply(get_max_sentiment, axis=1)
        df = df.drop(['neg', 'neu', 'pos', 'sentiment_score'], axis=1)
        return df
    else:
        return 


def print_sentiment(stock_info):
        df = get_sentiment_data(stock_info)
        #st.write("Market Sentiment")
        st.dataframe(df, hide_index =True )
        return df

 
def get_eod_forecast24(stock_info):
    symbol = stock_info.info['symbol']
    url_forecast_eod_with_ticker = f"{url_forecast_eod24}{symbol}"
    response = requests.get(url_forecast_eod_with_ticker)
    if response.status_code == 200:
        eod_forecast = json.loads(response.json())         
        #st.write(json_data)
        #eod_forecast = json_data["latest_prediction"]
        return eod_forecast
    else:
        return 
    
def get_eod_forecast(stock_info):
    symbol = stock_info.info['symbol']
    url_forecast_eod_with_ticker = f"{url_forecast_eod}{symbol}"
    response = requests.get(url_forecast_eod_with_ticker)
    if response.status_code == 200:
        json_data = json.loads(response.json())         
       
        eod_forecast = json_data["latest_prediction"]
        return eod_forecast
    else:
        return 
   
def print_sentiment_summery(df) :
    column_name = "sentiment"
    category_counts = df[column_name].value_counts()
    df_sentiment = pd.DataFrame({
        "Sentiment": category_counts.index,
        "Count": category_counts.values
    })
    st.dataframe(df_sentiment, hide_index =True )
    return df_sentiment



def print_stock_info(stock_info):
    stock_info_html = get_stock_info_from_html(stock_info.info)
    st.write(stock_info_html, unsafe_allow_html=True)
    plot_graph(stock_info)
    
    col1, col2 = st.columns([0.8, 0.2])
    with col1:
        st.pyplot(plt)
    with col2:
        tf = st.radio(
        "Select Time Frame",
        ["1Y", "3Y", "5Y", "10Y"], index=2,
        key="chart_time_frame",       
        )
        
def get_stock_info_from_html(stock_info):
    si = stock_info
    text = (f"<b>Comp. Name: </b> {si['longName']}, {si['city']}, {si.get('state', '')} {si['country']} <br>"
            f"<b>Web site: </b>   <a href=\"{si['website']}\">{si['website']}</a> <br>"
            f"<b>Stock Price: </b>  {si['currentPrice']} {str(si['financialCurrency'])}")
    return text



def get_forecast_html(stock_info):

    currentPrice = stock_info.info['currentPrice']
    eod_forecast = get_eod_forecast(stock_info)
    eod_forecast_price = currentPrice * (1 + eod_forecast/100)
    color = 'red' if eod_forecast < 0 else 'green'
    mark = '+' if eod_forecast >= 0 else '-'
    eod_forecast_p = abs(round(eod_forecast, 2))  
    html = (f"<b>Current Price: </b> {stock_info.info['currentPrice']} <br>"
            f"<b>EOD Close Price: </b> <span style='color:{color};'> {eod_forecast_price:.2f} </span> &emsp;  <span style='color:{color};'> {mark}{eod_forecast_p}% </span> ")
          
    return html
    
def get_forecast_html24(stock_info):

    currentPrice = stock_info.info['currentPrice']
    eod_forecast = get_eod_forecast24(stock_info)
    eod_forecast_price = currentPrice * (1 + eod_forecast/100)
    color = 'red' if eod_forecast < 0 else 'green'
    mark = '+' if eod_forecast >= 0 else '-'
    eod_forecast_p = abs(round(eod_forecast, 2))  
    html = (f"<b>EOD Tomorrow Close Price: </b> <span style='color:{color};'> {eod_forecast_price:.2f} </span> &emsp;  <span style='color:{color};'> {mark}{eod_forecast_p}% </span> ")
          
    return html 

def plot_graph(stock_info):
    period = st.session_state.chart_time_frame or "5Y" 
    history = stock_info.history(period=period)
    name = stock_info.info['longName']
    plt.plot(history['Close'])
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.title(f"{name} Stock Price")
    return plt



def print_timeGpt(stock_info):
    symbol = stock_info.info['symbol']
    url_timeGpt_with_ticker = f"{url_timeGpt}{symbol}"
    response = requests.get(url_timeGpt_with_ticker)
    if response.status_code == 200:
        json_data = json.loads(response.json()) 
        #st.write(json_data)
        json_data = json.loads(response.json()) 
 
        data = json_data["data"]
        converted_data = []

        for row in data:
            converted_data.append({"Date": row[0], "TimeGPT": row[1]}) 
  
        df = pd.DataFrame(converted_data)
        st.dataframe(df)
        return df
    else:
        return 
    
    
    
st.set_page_config(page_title="Senty Sense")
 
st.markdown(
        """
            <style>
                .appview-container .main .block-container {{
                    padding-top: {padding_top}rem;
                    padding-bottom: {padding_bottom}rem;
                    }}
            </style>""".format(
            padding_top=1, padding_bottom=1
        ),
        unsafe_allow_html=True,
    )


st.title('_SentySense_')  #PriceProphet, Sentyment, Trendsetter Bullseye
par1 = "Our stock market platform gives you real-time data, historical insights, and in-depth news to help you make informed investment decisions."
st.write(par1, unsafe_allow_html=True)
 
if 'chart_time_frame' not in st.session_state:
    st.session_state['chart_time_frame'] = '5Y'

if 'data_available' not in st.session_state:
    st.session_state['data_available'] = False


option =  'Stocks'  #st.selectbox("select", ["", "Currencies", "Stocks"], placeholder="Choose an option", label_visibility =  "hidden")

if option == "Currencies":
    input_text = "Enter currency pair"
else:
    input_text = "Enter stock symbol"


text_box: str = None
btn_get_data = None

if option: 
    text_box = st.text_input(input_text)
 
with st.spinner('Wait for it...'):
    if text_box:
        ticker = text_box.upper()
        try:
            stock_info = yf.Ticker(ticker)
            long_name = stock_info.info['longName']
            st.write(f"<H4>{long_name}</H4>", unsafe_allow_html=True)
        except:
            st.error('Ticker not found', icon="🚨")  
            st.session_state['data_available'] = False    
        else:
            st.session_state['data_available'] = True      
            print_stock_info(stock_info)
            st.write(f"<H4>Market Sentiment</H4>", unsafe_allow_html=True)
            df = print_sentiment(stock_info)
            st.write(f"<H6>Sentiment summery</H6>", unsafe_allow_html=True)
            print_sentiment_summery(df)
            st.write(f"<H4>Forecasting</H4>", unsafe_allow_html=True)
            st.write(get_forecast_html(stock_info), unsafe_allow_html=True) 
            st.write(get_forecast_html24(stock_info), unsafe_allow_html=True) 
            st.write("TimeGPT Monthly forecast")
            print_timeGpt(stock_info)