# -*- coding: utf-8 -*- """Untitled1.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1OpumpFAYHp3dJhfH9ZUWpQRDx9FqOVOd """ import requests from bs4 import BeautifulSoup import pandas as pd import matplotlib.pyplot as plt def extract_question_options(url): response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') tables = soup.find_all('table', class_='menu-tbl') question_ids = [] chosen_options = [] option_1_ids = [] option_2_ids = [] option_3_ids = [] option_4_ids = [] for table in tables: question_id = table.find('td', string='Question ID :').find_next('td').text chosen_option = table.find('td', string='Chosen Option :').find_next('td').text option_1_id = table.find('td', string='Option 1 ID :').find_next('td').text option_2_id = table.find('td', string='Option 2 ID :').find_next('td').text option_3_id = table.find('td', string='Option 3 ID :').find_next('td').text option_4_id = table.find('td', string='Option 4 ID :').find_next('td').text status = table.find('td', string='Status :').find_next('td').text if 'Not Answered' in status or 'Marked For Review' in status: chosen_option = 'Not Attempted' question_ids.append(question_id) chosen_options.append(chosen_option) option_1_ids.append(option_1_id) option_2_ids.append(option_2_id) option_3_ids.append(option_3_id) option_4_ids.append(option_4_id) data = { 'Question ID': question_ids, 'Chosen Option': chosen_options, 'Option 1 ID': option_1_ids, 'Option 2 ID': option_2_ids, 'Option 3 ID': option_3_ids, 'Option 4 ID': option_4_ids } df = pd.DataFrame(data) new_data = [] for _, row in df.iterrows(): chosen_option = row['Chosen Option'] question_id = row['Question ID'] if chosen_option == 'Not Attempted': option_id = 'Not Attempted' else: option_id = row[f'Option {chosen_option} ID'] new_data.append({'Question ID': question_id, 'My Options(s)': option_id}) new_df = pd.DataFrame(new_data) return new_df def extract_question_info(data): lines = data.split("\n") result = [] skip_row = False for line in lines: if line: if skip_row: skip_row = False continue parts = line.split("\t") question_id = parts[2] correct_option = "" for option in parts[3:]: if option != "None of These": correct_option = option break result.append({"Question ID": question_id, "Correct Option(s)": correct_option}) skip_row = True df = pd.DataFrame(result) return df def compare_answers(data, url): # Call extract_question_info to get the ans_df DataFrame ans_df = extract_question_info(data) # Call extract_question_options to get the new_df DataFrame new_df = extract_question_options(url) # Merge the two DataFrames based on the 'Question ID' column merged_df = ans_df.merge(new_df, on='Question ID', how='inner') # Compare the Correct Option(s) and My Options(s) columns and assign marks merged_df['Marks'] = merged_df.apply(lambda row: 4 if row['Correct Option(s)'] == row['My Options(s)'] else (-1 if row['My Options(s)'] != 'Not Attempted' else 0), axis=1) # Calculate total marks total_marks = len(ans_df) * 4 # Calculate number of wrong answers wrong_answers = len(merged_df[merged_df['Marks'] == -1]) # Calculate number of right answers right_answers = len(merged_df[merged_df['Marks'] == 4]) # Calculate number of not attempted questions not_attempted = len(new_df[new_df['My Options(s)'] == 'Not Attempted']) # Calculate marks obtained marks_obtained = merged_df['Marks'].sum() # Calculate percentage score percentage_score = (marks_obtained / total_marks) * 100 # Create the markdown text text = f"Total Marks: {total_marks}\n" text += f"Number of Wrong Answers: {wrong_answers}\n" text += f"Number of Right Answers: {right_answers}\n" text += f"Number of Not Attempted Questions: {not_attempted}\n" text += f"Marks Obtained: {marks_obtained}\n" text += f"Percentage Score: {percentage_score}\n" # Plotting the overall performance labels = ['Right Answers', 'Wrong Answers', 'Not Attempted'] sizes = [right_answers, wrong_answers, not_attempted] colors = ['#66BB6A', '#EF5350', '#FFA726'] plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90) plt.axis('equal') plt.title('Overall Performance') return text, merged_df, plt import gradio as gr with gr.Blocks(theme='gradio/soft') as demo: gr.Markdown(""" ## FOLLOW THIS STEPS TO EXTRACT THE DATA ![Image](https://i.imgur.com/9dzYJZ1.gif) """) data = gr.Textbox(label="Correct Options in The Website",placeholder= """1 Data Science Artificial Intelligence_Eng - PART A 123456789 987654321 987654321 987654322 987654323 987654324 None of These 2 Data Science Artificial Intelligence_Eng - PART A 234567890 123456789 123456789 123456790 123456791 123456792 None of These 3 Data Science Artificial Intelligence_Eng - PART A 345678901 234567890 234567890 234567891 234567892 234567893 None of These 4 Data Science Artificial Intelligence_Eng - PART A 456789012 345678901 345678901 345678902 345678903 345678904 None of These 5 Data Science Artificial Intelligence_Eng - PART A 567890123 456789012 456789012 456789013 456789014 456789015 None of These 6 Data Science Artificial Intelligence_Eng - PART A 678901234 567890123 567890123 567890124 567890125 567890126 None of These 7 Data Science Artificial Intelligence_Eng - PART A 789012345 678901234 678901234 678901235 678901236 678901237 None of These 8 Data Science Artificial Intelligence_Eng - PART A 890123456 789012345 789012345 789012346 789012347 789012348 None of These 9 Data Science Artificial Intelligence_Eng - PART A 901234567 890123456 890123456 890123457 890123458 890123459 None of These 10 Data Science Artificial Intelligence_Eng - PART A 123456789 901234567 901234567 901234568 901234569 901234570 None of These 11 Data Science Artificial Intelligence_Eng - PART A 234567890 123456789 123456789 123456790 123456791 123456792 None of These 12 Data Science Artificial Intelligence_Eng - PART A 345678901 234567890 234567890 234567891 234567892 234567893 None of These 13 Data Science Artificial Intelligence_Eng - PART A 456789012 345678901 345678901 345678902 345678903 . . . . 95 Data Science Artificial Intelligence_Eng - PART A 678901234 567890123 567890123 567890124 567890125 567890126 None of These 96 Data Science Artificial Intelligence_Eng - PART A 789012345 678901234 678901234 678901235 678901236 678901237 None of These 97 Data Science Artificial Intelligence_Eng - PART A 890123456 789012345 789012345 789012346 789012347 789012348 None of These 98 Data Science Artificial Intelligence_Eng - PART A 901234567 890123456 890123456 890123457 890123458 890123459 None of These 99 Data Science Artificial Intelligence_Eng - PART A 123456789 901234567 901234567 901234568 901234569 901234570 None of These 100 Data Science Artificial Intelligence_Eng - PART A 234567890 123456789 123456789 123456790 123456791 123456792 None of These""", lines=5) gr.Markdown("![Image](https://i.ibb.co/FVwGm6L/Screenshot-179.png)") url = gr.Textbox(label="Link to your Answers URL",placeholder="https://cdn3.digialm.com//per/g28/pub/XXXX/touchstone/AssessmentQPHTMLMode1//XXXXXXXX/XXXXXXXX/XXXXXXXX/XXXXXXXXXXX.html") btn = gr.Button(value="Check Your Answer!") out = gr.Textbox(value="", label="Output") out1 = gr.Plot() out2=gr.Dataframe() btn.click(compare_answers, inputs=[data, url], outputs=[out,out2,out1]) gr.Markdown("Made with :heart: by Neelanjan Chakraborty") if __name__ == "__main__": demo.launch(debug= True,share=True)