Spaces:

Harshil24
/

AI_prihars

Sleeping

App Files Files Community

Harshil24 commited on Sep 17, 2023

Commit

694eae1

1 Parent(s): f54a21b

Update app.py

Browse files

Files changed (1) hide show

app.py +170 -1

app.py CHANGED Viewed

@@ -1,3 +1,172 @@
 import subprocess
-subprocess.run("ls")

+from bs4 import BeautifulSoup
+import csv
+import requests
+import numpy as np
+import pandas as pd
+import os
+import datetime
+from tensorflow import keras
 import subprocess
+def getdata():
+#------------------------GETTING HTML TABLE DATA---------------------------
+  url = f'https://ltp.investingdaddy.com/detailed-options-chain.php'
+  response = requests.get(url)
+  if response.status_code == 200:
+      html_source = response.text
+      #print(html_source)
+  else:
+      print(f"Failed to retrieve the webpage. Status code: {response.status_code}")
+  #------------------------FILTERING TABLE DATA-------------------------------
+  soup = BeautifulSoup(html_source, 'html.parser')
+  tables = soup.find_all('table')
+  ##price = soup.find('label', {'id':'future_val'})
+  ##price = price.text
+  ##prices.append(price)
+  ##return
+  if len(tables) >= 2:
+      second_table = tables[1]
+  else:
+      print("There are not enough tables in the HTML source.")
+  #-----------------------CONVERTING HTML TABLE DATA TO CSV--------------------
+  html_content = "<html>"+str(second_table)+"</html>"
+  soup = BeautifulSoup(html_content, 'html.parser')
+  table = soup.find('table', {'id': 'tech-companies-1'})
+  table_data = []
+  rows = table.find_all('tr')
+  for row in rows:
+      row_data = []
+      cols = row.find_all(['th', 'td'])
+      for col in cols:
+          row_data.append(col.get_text(strip=True))
+      table_data.append(row_data)
+  csv_file = 'sample.csv'
+  r=False
+  with open(csv_file, 'w', newline='') as csvfile:
+      csv_writer = csv.writer(csvfile)
+      for row_data in table_data:
+        if r:
+          csv_writer.writerow(row_data)
+        else:
+          r=True
+  print(f'Table data has been successfully written to {csv_file}.')
+def get_his_data(m,d,h,min):
+#------------------------GETTING HTML TABLE DATA---------------------------
+  url = f'https://ltp.investingdaddy.com/historical-option-chain.php?symbol=NIFTY&expiry=2023-09-07&filterdate1=2023-09-04&filtertime=09%3A15&filterdate=2023-{m}-{d}T{h}%3A{min}'
+  response = requests.get(url)
+  if response.status_code == 200:
+      html_source = response.text
+      #print(html_source)
+  else:
+      print(f"Failed to retrieve the webpage. Status code: {response.status_code}")
+  #------------------------FILTERING TABLE DATA-------------------------------
+  soup = BeautifulSoup(html_source, 'html.parser')
+  tables = soup.find_all('table')
+  ##price = soup.find('label', {'id':'future_val'})
+  ##price = price.text
+  ##prices.append(price)
+  ##return
+  if len(tables) >= 2:
+      second_table = tables[1]
+  else:
+      print("There are not enough tables in the HTML source.")
+  #-----------------------CONVERTING HTML TABLE DATA TO CSV--------------------
+  html_content = "<html>"+str(second_table)+"</html>"
+  soup = BeautifulSoup(html_content, 'html.parser')
+  table = soup.find('table', {'id': 'tech-companies-1'})
+  table_data = []
+  rows = table.find_all('tr')
+  for row in rows:
+      row_data = []
+      cols = row.find_all(['th', 'td'])
+      for col in cols:
+          row_data.append(col.get_text(strip=True))
+      table_data.append(row_data)
+  csv_file = 'sample.csv'
+  r=False
+  with open(csv_file, 'w', newline='') as csvfile:
+      csv_writer = csv.writer(csvfile)
+      for row_data in table_data:
+        if r:
+          csv_writer.writerow(row_data)
+        else:
+          r=True
+  print(f'historical Table data has been successfully written to {csv_file}.')
+def changedatashape():
+  # Load your CSV data into a Pandas DataFrame
+  df = pd.read_csv('sample.csv')  # Replace 'your_data.csv' with your actual file path
+  # Check the shape of the original DataFrame (it should be 20x20)
+  #print("Original DataFrame Shape:", df.shape)
+  # Convert the DataFrame into a NumPy array
+  data = df.to_numpy()
+  # Reshape the data into a 1D array and transpose it
+  horizontal_data = data.flatten().reshape(1, -1)
+  # Check the shape of the reshaped data (it should be 1x400)
+  #print("Horizontal Data Shape:", horizontal_data.shape)
+  # If you want to save this reshaped data to a new CSV file:
+  horizontal_df = pd.DataFrame(horizontal_data)
+  # Save it to a new CSV file without row or column labels
+  horizontal_df.to_csv('sample2.csv', index=False, header=False)
+  os.remove('sample.csv')
+def predict(m,historical=True):
+  if historical:
+    date = input('date:')
+    hour = input('hour:')
+    min = input('min')
+    get_his_data(m,date,hour,min)
+  else:
+    getdata()
+  changedatashape()
+  # Specify the path to your CSV file
+  csv_file_path = 'sample2.csv'
+  # Read the existing data to determine the number of columns and rows
+  with open(csv_file_path, 'r') as csv_file:
+      reader = csv.reader(csv_file)
+      data = list(reader)
+      num_columns = len(data[0]) if data else 0
+      num_rows = len(data)
+  # Generate the header row for the new column
+  new_column_header = 'sample names'
+  # Generate the values for the new column
+  new_column_values = ['sample{}'.format(i-1) for i in range(1, num_rows + 1)]
+  # Insert the new column into the data
+  for i in range(num_rows):
+      data[i].insert(0, new_column_values[i])
+  # Write the modified data back to the file
+  with open(csv_file_path, 'w', newline='') as csv_file:
+      writer = csv.writer(csv_file)
+      writer.writerow([new_column_header] + ['feature{}'.format(i+1) for i in range(1, num_columns + 1)])
+      writer.writerows(data)
+if st.button("Generate Text"):
+    predict(historical=False)