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| # -*- 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 | |
|  | |
| """) | |
| 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("") | |
| 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) | |