Update main.py

main.py

@@ -8,14 +8,7 @@ from bs4 import BeautifulSoup
 import pandas as pd
 import json  # for graph plotting in website
 
-# NLTK VADER for sentiment analysis
-import nltk
 
-nltk.downloader.download("vader_lexicon")
-from nltk.sentiment.vader import SentimentIntensityAnalyzer
-
-import subprocess
-import os
 
 import datetime
 
@@ -31,109 +24,13 @@ app = FastAPI()
 @app.get("/")
 def read_root():
     return {
-        "message": "Hello, Please type a ticker at the end of the URL to get the stock sentiment.",
-        "format": "https://yaakovy-fin-proj-docker.hf.space/ticker/[TICKER]",
-        "example": "https://yaakovy-fin-proj-docker.hf.space/ticker/msft",
-    }
-
-
-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)
-    # Read the contents of the file into 'html'
-    html = BeautifulSoup(response)
-    # Find 'news-table' in the Soup and load it into 'news_table'
-    news_table = html.find(id="news-table")
-    return news_table
-
-
-# parse news into dataframe
-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
-            # get text from a only
-            text = x.a.get_text()
-            # splite text in the td tag into a list
-            date_scrape = x.td.text.split()
-            # if the length of 'date_scrape' is 1, load 'time' as the only element
-
-            if len(date_scrape) == 1:
-                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]
-
-            # Append ticker, date, time and headline as a list to the 'parsed_news' list
-            parsed_news.append([date, time, text])
-        except:
-            pass
-
-        # Set column names
-        columns = ["date", "time", "headline"]
-        # Convert the parsed_news list into a DataFrame called 'parsed_and_scored_news'
-        parsed_news_df = pd.DataFrame(parsed_news, columns=columns)
-        # Create a pandas datetime object from the strings in 'date' and 'time' column
-        parsed_news_df["date"] = parsed_news_df["date"].replace("Today", today_string)
-        # parsed_news_df["datetime"] = pd.to_datetime(
-        #     parsed_news_df["date"] + " " + parsed_news_df["time"],
-        #     format="%Y-%m-%d %H:%M",
-        # )
-
-    return parsed_news_df
-
-
-def score_news(parsed_news_df):
-    # Instantiate the sentiment intensity analyzer
-    vader = SentimentIntensityAnalyzer()
-
-    # Iterate through the headlines and get the polarity scores using vader
-    scores = parsed_news_df["headline"].apply(vader.polarity_scores).tolist()
-
-    # Convert the 'scores' list of dicts into a DataFrame
-    scores_df = pd.DataFrame(scores)
-
-    # Join the DataFrames of the news and the list of dicts
-    parsed_and_scored_news = parsed_news_df.join(scores_df, rsuffix="_right")
-    # parsed_and_scored_news = parsed_and_scored_news.set_index("datetime")
-    parsed_and_scored_news = parsed_and_scored_news.drop(["date", "time"], axis=1)
-    parsed_and_scored_news = parsed_and_scored_news.rename(
-        columns={"compound": "sentiment_score"}
-    )
-    return parsed_and_scored_news
+        "message": "Hello, Please type a ticker at the end of the URL to get the stock sentiment."
 
-
-# for extracting data from finviz
-finviz_url = "https://finviz.com/quote.ashx?t="
-
-
-def get_stock_data(ticker):
-    news_table = get_news(ticker)
-    parsed_news_df = parse_news(news_table)
-    parsed_and_scored_news = score_news(parsed_news_df)
-    return parsed_and_scored_news
-
-
-@app.get("/ticker/{ticker}")
-def read_item(ticker: str):
-    stock_data = get_stock_data(ticker)
-    result = stock_data.to_json(orient="records")
-    return result
+    }
 
 ## TimeGPT 
     
-@app.get("/ticker/{ticker}")
+
 def get_data_from_yahoo(ticker: str):
     yf.pdr_override()
     data = pdr.get_data_yahoo(ticker, start="2023-01-01", end=None)
@@ -151,12 +48,12 @@ def apikey(api_key):
     key_valid = nixtla_client.validate_api_key()
     return key_valid
 
-def forecasting(key_valid,df):    
-    if key_valid == True:
+def forecasting(df):    
+   ## if key_valid == True:
 ## forecasting 3 months     
        timegpt_fcst_df = nixtla_client.forecast(df=df, h=3, freq='MS', time_col='Date', target_col='Adj Close')
-    else:
-       timegpt_fcst_df= 'API key is not Valid'
+    # else:
+    #    timegpt_fcst_df= 'API key is not Valid'
         
     return timegpt_fcst_df, nixtla_client.plot(df, timegpt_fcst_df, time_col='Date', target_col='Adj Close')
 
@@ -170,4 +67,14 @@ def anomaly_detect(df):
              timegpt_anomalies_df,
              time_col='Date',
              target_col='Adj Close')
+
+
+@app.get("/ticker/{ticker}")
+def read_item(ticker: str):
+    data = get_data_from_yahoo(ticker)
+    stock_forecast = forecasting(df)
+    result = stock_forecast.to_json()
+    return result
+
+