Update app.py

app.py

@@ -1,4 +1,3 @@
-import time
 import streamlit as st
 import pandas as pd
 import yfinance as yf
@@ -6,69 +5,19 @@ import matplotlib.pyplot as plt
 import requests
 import json
  
-from urllib.request import urlopen, Request
-from bs4 import BeautifulSoup
+
+ 
 import pandas as pd
  
-import nltk
-nltk.downloader.download("vader_lexicon")
-from nltk.sentiment.vader import SentimentIntensityAnalyzer
-
-
 
 
 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/"
-finviz_url = "https://finviz.com/quote.ashx?t="
+url_forecast_eod = "https://yaakovy-lasthourforcast.hf.space/ticker/"  
  
 
-def get_news(ticker):
-    url = finviz_url + ticker
-    req = Request(
-        url=url,
-        headers={
-            "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) Gecko/20100101 Firefox/20.0"
-        },
-    )
-    response = urlopen(req)
-    if response.status != 200:   # Check the response status code
-        raise Exception("Failed to fetch news table")
-    html = BeautifulSoup(response)   # Read the contents of the file into 'html'
-    news_table = html.find(id="news-table")   # Find 'news-table' in the Soup and load it into 'news_table'
-    return news_table
-
-
-def parse_news(news_table):
-    parsed_news = []
-    today_string = datetime.datetime.today().strftime("%Y-%m-%d")
-
-    for x in news_table.findAll("tr"):
-        try:
-            # read the text from each tr tag into text           
-            text = x.a.get_text() # get text from a only          
-            date_scrape = x.td.text.split()  # splite text in the td tag into a list
-            if len(date_scrape) == 1: # if the length of 'date_scrape' is 1, load 'time' as the only element
-                time = date_scrape[0]
-            # else load 'date' as the 1st element and 'time' as the second
-            else:
-                date = date_scrape[0]
-                time = date_scrape[1]
-            parsed_news.append([date, time, text])         # Append ticker, date, time and headline as a list to the 'parsed_news' list
-        except:
-            pass
-        columns = ["date", "time", "headline"]   
-        parsed_news_df = pd.DataFrame(parsed_news, columns=columns) # Convert the parsed_news list into a DataFrame called 'parsed_and_scored_news'  
-    return parsed_news_df
-
-def score_news(parsed_news_df):
-    vader = SentimentIntensityAnalyzer()  # Instantiate the sentiment intensity analyzer
-    scores = parsed_news_df["headline"].apply(vader.polarity_scores).tolist()  # Iterate through the headlines and get the polarity scores using vader
-    scores_df = pd.DataFrame(scores)    # Convert the 'scores' list of dicts into a DataFrame
-    parsed_and_scored_news = parsed_news_df.join(scores_df, rsuffix="_right")  # Join the DataFrames of the news and the list of dicts
-    parsed_and_scored_news = parsed_and_scored_news.rename(columns={"compound": "sentiment"})
-    return parsed_and_scored_news
-
+ 
 def get_max_sentiment(row):
     if row["sentiment_score"] > 0.05:  # Threshold for positive sentiment
         return "positive"
@@ -92,13 +41,28 @@ def get_sentiment_data(stock_info):
     else:
         return 
 
+
 def print_sentiment(stock_info):
         df = get_sentiment_data(stock_info)
-        st.write("Market Sentiment")
+        #st.write("Market Sentiment")
         st.dataframe(df, hide_index =True )
         return df
+
+ 
+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)  :
+
+   
+def print_sentiment_summery(df) :
     column_name = "sentiment"
     category_counts = df[column_name].value_counts()
     df_sentiment = pd.DataFrame({
@@ -127,12 +91,29 @@ def print_stock_info(stock_info):
         
 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)
+    #st.write(eod_forecast)
+    #eod_forecast = float(get_eod_forecast(stock_info))
+    #st.write(type(eod_forecast))
+    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 plot_graph(stock_info):
     period = st.session_state.chart_time_frame or "5Y" 
     history = stock_info.history(period=period)
@@ -177,7 +158,6 @@ st.markdown(
                     padding-top: {padding_top}rem;
                     padding-bottom: {padding_bottom}rem;
                     }}
-
             </style>""".format(
             padding_top=1, padding_bottom=1
         ),
@@ -221,15 +201,17 @@ with st.spinner('Wait for it...'):
             st.error('Ticker not found', icon="🚨")  
             st.session_state['data_available'] = False    
         else:
-            
-            st.session_state['data_available'] = True          
+            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('Sentiment summery')
+            st.write(f"<H6>Sentiment summery</H6>", unsafe_allow_html=True)
             print_sentiment_summery(df)
-            st.write('Prediction')
-            print_timeGpt(stock_info)
-
-
-
+            st.write(f"<H4>Prediction</H4>", unsafe_allow_html=True)
+            st.write(get_forecast_html(stock_info), unsafe_allow_html=True) 
+            
+            
+            #st.write(stock_info)
+            #st.write(eod_forecast)
+            #print_timeGpt(stock_info)