Update app.py

app.py

@@ -3,18 +3,16 @@ import yfinance as yf
 import pandas as pd
 import cufflinks as cf
 import datetime
-import datetime.datetime as dt
 import plotly.graph_objects as go
 from bs4 import BeautifulSoup
 import requests
 import os
-from datetime import date, timedelta, datetime
+from datetime import date, timedelta
 
 # App title
 st.markdown('''
 # Sovrenn Market Sentiment Indicator App
 Shown are the stock price data for the selected company!
-
 **Credits**
 - App built by SRL
 ''')
@@ -84,72 +82,70 @@ if tickerSymbol:
 
 
 
-else:
-    st.warning("Please enter a valid Stock Ticker Symbol.")
 
 
 
 
-d0 = start_date
-d1 = dt.date(2008, 1, 1)
-delta = d0 - d1
 
-st.write(delta)
+    d0 = start_date
+    d1 = datetime.date(2008, 1, 1)
+    delta = d0 - d1
 
-Begindatestring = start_date
+    st.write(delta)
 
+    Begindatestring = start_date
 
-#Begindatestring = datetime.strptime(Begindatestring, "%Y-%m-%d").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)
+    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
 
-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
+    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])
 
+    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
+    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")
+    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")
 
+    tokenizer = AutoTokenizer.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
+    model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-finetuned-finance-classification")
 
 
 
@@ -159,55 +155,55 @@ model = AutoModelForSequenceClassification.from_pretrained("nickmuchi/sec-bert-f
 
 
 
-nlp = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)
 
-length = len(news[ 'News'].to_list())
-news_list = news[ 'News'].to_list() 
+    nlp = pipeline("text-classification", model=model, tokenizer=tokenizer, device=device)
 
-df = pd.DataFrame()
-for i in range (0, length):
+    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"]
 
+        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']
+    #st.dataframe(df)
 
-# Calculate the sum of the 'Score' column for "neutral" rows
-bullish_score_sum = bullish_rows['score'].sum()
+    # Filter the DataFrame to get rows with "neutral" sentiment
+    bullish_rows = df[df['label'] == 'bullish']
 
-num_bullish_rows = len(bullish_rows)
-# Calculate the average score for "neutral" sentiment
-average_score_for_bullish = bullish_score_sum / num_bullish_rows
+    # 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()
+    # Filter the DataFrame to get rows with "neutral" sentiment
+    bearish_rows = df[df['label'] == 'bearish']
 
-# Cabearishlculate the number of "neutral" rows
-num_bearish_rows = len(bearish_rows)
+    # Calculate the sum of the 'Score' column for "neutral" rows
+    bearish_score_sum = bearish_rows['score'].sum()
 
-# 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")
+    # 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")
 
 
 
+else:
+    st.warning("Please enter a valid Stock Ticker Symbol.")