app.py

import streamlit as st
from transformers import pipeline
from PIL import Image

#st.write('importing selenium')
# import selenium
# from selenium import webdriver
# from selenium.webdriver.common.by import By
# from selenium.webdriver.common.keys import Keys

pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")

driver = webdriver.Chrome()  

driver.get('https://www.nseindia.com/all-reports-derivatives#cr_deriv_equity_archives')

# Maximize Window
driver.maximize_window() 
driver.implicitly_wait(10)

date_elements = driver.find_elements(By.XPATH,"//button[@aria-label='Datepicker button']")

date_elements[0].click()

# st.title("Hot Dog? Or Not?")


# file_name = st.file_uploader("Upload a hot dog candidate image")

# if file_name is not None:
#     col1, col2 = st.columns(2)

#     image = Image.open(file_name)
#     col1.image(image, use_column_width=True)
#     predictions = pipeline(image)

#     col2.header("Probabilities")
#     for p in predictions:
#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")