Upload cuet.py

cuet.py

@@ -0,0 +1,216 @@
+# -*- 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("""
+
+if __name__ == "__main__":
+    demo.launch(debug= True,share=True)
+
+
+