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| # from ragatouille import RAGPretrainedModel | |
| # import subprocess | |
| # import json | |
| # import spaces | |
| # import firebase_admin | |
| # from firebase_admin import credentials, firestore | |
| # import logging | |
| # from pathlib import Path | |
| # from time import perf_counter | |
| # from datetime import datetime | |
| # import gradio as gr | |
| # from jinja2 import Environment, FileSystemLoader | |
| # import numpy as np | |
| # from sentence_transformers import CrossEncoder | |
| # from huggingface_hub import InferenceClient | |
| # from os import getenv | |
| # from backend.query_llm import generate_hf, generate_openai | |
| # from backend.semantic_search import table, retriever | |
| # from huggingface_hub import InferenceClient | |
| # VECTOR_COLUMN_NAME = "vector" | |
| # TEXT_COLUMN_NAME = "text" | |
| # HF_TOKEN = getenv("HUGGING_FACE_HUB_TOKEN") | |
| # proj_dir = Path(__file__).parent | |
| # # Setting up the logging | |
| # logging.basicConfig(level=logging.INFO) | |
| # logger = logging.getLogger(__name__) | |
| # client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1",token=HF_TOKEN) | |
| # # Set up the template environment with the templates directory | |
| # env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) | |
| # # Load the templates directly from the environment | |
| # template = env.get_template('template.j2') | |
| # template_html = env.get_template('template_html.j2') | |
| # def system_instructions(question_difficulty, topic,documents_str): | |
| # return f"""<s> [INST] Your are a great teacher and your task is to create 10 questions with 4 choices with a {question_difficulty} difficulty about topic request " {topic} " only from the below given documents, {documents_str} then create an answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". [/INST]""" | |
| # RAG_db=gr.State() | |
| # with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT: | |
| # def load_model(): | |
| # RAG= RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0") | |
| # RAG_db.value=RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index') | |
| # return 'Ready to Go!!' | |
| # with gr.Column(scale=4): | |
| # gr.HTML(""" | |
| # <center> | |
| # <h1><span style="color: purple;">ADWITIYA</span> Customs Manual Quizbot</h1> | |
| # <h2>Generative AI-powered Capacity building for Training Officers</h2> | |
| # <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i> | |
| # </center> | |
| # """) | |
| # #gr.Warning('Retrieving using ColBERT.. First time query will take a minute for model to load..pls wait') | |
| # with gr.Column(scale=2): | |
| # load_btn = gr.Button("Click to Load!🚀") | |
| # load_text=gr.Textbox() | |
| # load_btn.click(load_model,[],load_text) | |
| # topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual") | |
| # with gr.Row(): | |
| # radio = gr.Radio( | |
| # ["easy", "average", "hard"], label="How difficult should the quiz be?" | |
| # ) | |
| # generate_quiz_btn = gr.Button("Generate Quiz!🚀") | |
| # quiz_msg=gr.Textbox() | |
| # question_radios = [gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio( | |
| # visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio(visible=False), gr.Radio( | |
| # visible=False), gr.Radio(visible=False), gr.Radio(visible=False)] | |
| # print(question_radios) | |
| # @spaces.GPU | |
| # @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg]+question_radios, api_name="generate_quiz") | |
| # def generate_quiz(question_difficulty, topic): | |
| # top_k_rank=10 | |
| # RAG_db_=RAG_db.value | |
| # documents_full=RAG_db_.search(topic,k=top_k_rank) | |
| # gr.Warning('Generation of Quiz may take 1 to 2 minute. Pls wait') | |
| # generate_kwargs = dict( | |
| # temperature=0.2, | |
| # max_new_tokens=4000, | |
| # top_p=0.95, | |
| # repetition_penalty=1.0, | |
| # do_sample=True, | |
| # seed=42, | |
| # ) | |
| # question_radio_list = [] | |
| # count=0 | |
| # while count<=3: | |
| # try: | |
| # documents=[item['content'] for item in documents_full] | |
| # document_summaries = [f"[DOCUMENT {i+1}]: {summary}{count}" for i, summary in enumerate(documents)] | |
| # documents_str='\n'.join(document_summaries) | |
| # formatted_prompt = system_instructions( | |
| # question_difficulty, topic,documents_str) | |
| # print(formatted_prompt) | |
| # pre_prompt = [ | |
| # {"role": "system", "content": formatted_prompt} | |
| # ] | |
| # response = client.text_generation( | |
| # formatted_prompt, **generate_kwargs, stream=False, details=False, return_full_text=False, | |
| # ) | |
| # output_json = json.loads(f"{response}") | |
| # print(response) | |
| # print('output json', output_json) | |
| # global quiz_data | |
| # quiz_data = output_json | |
| # for question_num in range(1, 11): | |
| # question_key = f"Q{question_num}" | |
| # answer_key = f"A{question_num}" | |
| # question = quiz_data.get(question_key) | |
| # answer = quiz_data.get(quiz_data.get(answer_key)) | |
| # if not question or not answer: | |
| # continue | |
| # choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)] | |
| # choice_list = [] | |
| # for choice_key in choice_keys: | |
| # choice = quiz_data.get(choice_key, "Choice not found") | |
| # choice_list.append(f"{choice}") | |
| # radio = gr.Radio(choices=choice_list, label=question, | |
| # visible=True, interactive=True) | |
| # question_radio_list.append(radio) | |
| # if len(question_radio_list)==10: | |
| # break | |
| # else: | |
| # print('10 questions not generated . So trying again!') | |
| # count+=1 | |
| # continue | |
| # except Exception as e: | |
| # count+=1 | |
| # print(f"Exception occurred: {e}") | |
| # if count==3: | |
| # print('Retry exhausted') | |
| # gr.Warning('Sorry. Pls try with another topic !') | |
| # else: | |
| # print(f"Trying again..{count} time...please wait") | |
| # continue | |
| # print('Question radio list ' , question_radio_list) | |
| # return ['Quiz Generated!']+ question_radio_list | |
| # check_button = gr.Button("Check Score") | |
| # score_textbox = gr.Markdown() | |
| # @check_button.click(inputs=question_radios, outputs=score_textbox) | |
| # def compare_answers(*user_answers): | |
| # user_anwser_list = [] | |
| # user_anwser_list = user_answers | |
| # answers_list = [] | |
| # for question_num in range(1, 20): | |
| # answer_key = f"A{question_num}" | |
| # answer = quiz_data.get(quiz_data.get(answer_key)) | |
| # if not answer: | |
| # break | |
| # answers_list.append(answer) | |
| # score = 0 | |
| # for item in user_anwser_list: | |
| # if item in answers_list: | |
| # score += 1 | |
| # if score>5: | |
| # message = f"### Good ! You got {score} over 10!" | |
| # elif score>7: | |
| # message = f"### Excellent ! You got {score} over 10!" | |
| # else: | |
| # message = f"### You got {score} over 10! Dont worry . You can prepare well and try better next time !" | |
| # return message | |
| # QUIZBOT.queue() | |
| # QUIZBOT.launch(debug=True) | |
| # ################################################ | |
| # from ragatouille import RAGPretrainedModel | |
| # import subprocess | |
| # import json | |
| # import spaces | |
| # import firebase_admin | |
| # from firebase_admin import credentials, firestore | |
| # import logging | |
| # from pathlib import Path | |
| # from time import perf_counter | |
| # from datetime import datetime | |
| # import gradio as gr | |
| # from jinja2 import Environment, FileSystemLoader | |
| # import numpy as np | |
| # from sentence_transformers import CrossEncoder | |
| # from os import getenv | |
| # from backend.query_llm import generate_hf, generate_openai | |
| # from backend.semantic_search import table, retriever | |
| # from gradio_client import Client # Modified here | |
| # VECTOR_COLUMN_NAME = "vector" | |
| # TEXT_COLUMN_NAME = "text" | |
| # proj_dir = Path(__file__).parent | |
| # # Setting up the logging | |
| # logging.basicConfig(level=logging.INFO) | |
| # logger = logging.getLogger(__name__) | |
| # # Replace Mixtral client with Qwen Client | |
| # client = Client("Qwen/Qwen1.5-110B-Chat-demo") # Modified here | |
| # # Set up the template environment with the templates directory | |
| # env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) | |
| # # Load the templates directly from the environment | |
| # template = env.get_template('template.j2') | |
| # template_html = env.get_template('template_html.j2') | |
| # def system_instructions(question_difficulty, topic, documents_str): | |
| # return f"""<s> [INST] You are a great teacher and your task is to create 10 questions with 4 choices with {question_difficulty} difficulty about the topic request "{topic}" only from the below given documents, {documents_str}. Then create answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". example is 'A10':'Q10:C3' [/INST]""" | |
| # # RA | |
| # RAG_db = gr.State() | |
| # with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT: | |
| # def load_model(): | |
| # RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0") | |
| # RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index') | |
| # return 'Ready to Go!!' | |
| # with gr.Column(scale=4): | |
| # gr.HTML(""" | |
| # <center> | |
| # <h1><span style="color: purple;">ADWITIYA</span> Customs Manual Quizbot</h1> | |
| # <h2>Generative AI-powered Capacity building for Training Officers</h2> | |
| # <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i> | |
| # </center> | |
| # """) | |
| # with gr.Column(scale=2): | |
| # load_btn = gr.Button("Click to Load!🚀") | |
| # load_text = gr.Textbox() | |
| # load_btn.click(load_model, [], load_text) | |
| # topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual") | |
| # with gr.Row(): | |
| # radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?") | |
| # generate_quiz_btn = gr.Button("Generate Quiz!🚀") | |
| # quiz_msg = gr.Textbox() | |
| # question_radios = [gr.Radio(visible=False) for _ in range(10)] | |
| # @spaces.GPU | |
| # @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg] + question_radios, api_name="generate_quiz") | |
| # def generate_quiz(question_difficulty, topic): | |
| # top_k_rank = 10 | |
| # RAG_db_ = RAG_db.value | |
| # documents_full = RAG_db_.search(topic, k=top_k_rank) | |
| # gr.Warning('Generation of Quiz may take 1 to 2 minutes. Please wait.') | |
| # question_radio_list = [] | |
| # count = 0 | |
| # while count <= 3: | |
| # try: | |
| # documents = [item['content'] for item in documents_full] | |
| # document_summaries = [f"[DOCUMENT {i + 1}]: {summary}{count}" for i, summary in enumerate(documents)] | |
| # documents_str = '\n'.join(document_summaries) | |
| # formatted_prompt = system_instructions(question_difficulty, topic, documents_str) | |
| # print(formatted_prompt) | |
| # # Use Qwen Client for quiz generation | |
| # response = client.predict( | |
| # query=formatted_prompt, | |
| # history=[], | |
| # system="You are a helpful assistant.", # Modified to match Qwen's API | |
| # api_name="/model_chat" | |
| # ) | |
| # print(response) | |
| # response1=response[1][0][1] | |
| # # Find the first and last curly braces | |
| # start_index = response1.find('{') | |
| # end_index = response1.rfind('}') | |
| # # Extract only the JSON part | |
| # if start_index != -1 and end_index != -1: | |
| # cleaned_response = response1[start_index:end_index + 1] # Include the last closing brace | |
| # # Try parsing the cleaned JSON | |
| # try: | |
| # output_json = json.loads(cleaned_response) | |
| # print('Parsed JSON:', output_json) # Successfully print the dictionary | |
| # except json.JSONDecodeError as e: | |
| # print(f"Failed to decode JSON: {e}") | |
| # else: | |
| # print("No valid JSON found in the response.") | |
| # # output_json = json.loads(f"{response}") | |
| # # print('output json', output_json) | |
| # global quiz_data | |
| # quiz_data = output_json | |
| # for question_num in range(1, 11): | |
| # question_key = f"Q{question_num}" | |
| # answer_key = f"A{question_num}" | |
| # question = quiz_data.get(question_key) | |
| # answer = quiz_data.get(quiz_data.get(answer_key)) | |
| # if not question or not answer: | |
| # continue | |
| # choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)] | |
| # choice_list = [quiz_data.get(choice_key, "Choice not found") for choice_key in choice_keys] | |
| # radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True) | |
| # question_radio_list.append(radio) | |
| # print('question_radio_list',question_radio_list) | |
| # if len(question_radio_list) == 10: | |
| # break | |
| # else: | |
| # print('10 questions not generated. Trying again!') | |
| # count += 1 | |
| # continue | |
| # except Exception as e: | |
| # count += 1 | |
| # print(f"Exception occurred: {e}") | |
| # if count == 3: | |
| # print('Retry exhausted') | |
| # gr.Warning('Sorry. Please try with another topic!') | |
| # else: | |
| # print(f"Trying again.. {count} time... please wait") | |
| # continue | |
| # return ['Quiz Generated!'] + question_radio_list | |
| # check_button = gr.Button("Check Score") | |
| # score_textbox = gr.Markdown() | |
| # @check_button.click(inputs=question_radios, outputs=score_textbox) | |
| # def compare_answers(*user_answers): | |
| # user_anwser_list = list(user_answers) | |
| # answers_list = [] | |
| # for question_num in range(1, 20): | |
| # answer_key = f"A{question_num}" | |
| # answer = quiz_data.get(quiz_data.get(answer_key)) | |
| # if not answer: | |
| # break | |
| # answers_list.append(answer) | |
| # score = sum(1 for item in user_anwser_list if item in answers_list) | |
| # if score > 5: | |
| # message = f"### Good! You got {score} out of 10!" | |
| # elif score > 7: | |
| # message = f"### Excellent! You got {score} out of 10!" | |
| # else: | |
| # message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!" | |
| # return message | |
| # QUIZBOT.queue() | |
| # QUIZBOT.launch(debug=True) | |
| # ##############?????????????????????????????? | |
| # import pandas as pd | |
| # import json | |
| # import gradio as gr | |
| # from pathlib import Path | |
| # from ragatouille import RAGPretrainedModel | |
| # from gradio_client import Client | |
| # from jinja2 import Environment, FileSystemLoader | |
| # from tempfile import NamedTemporaryFile | |
| # VECTOR_COLUMN_NAME = "vector" | |
| # TEXT_COLUMN_NAME = "text" | |
| # #proj_dir = Path(__file__).parent | |
| # proj_dir = Path.cwd() | |
| # # Setting up the logging | |
| # import logging | |
| # logging.basicConfig(level=logging.INFO) | |
| # logger = logging.getLogger(__name__) | |
| # # Replace Mixtral client with Qwen Client | |
| # client = Client("Qwen/Qwen1.5-110B-Chat-demo") | |
| # # Set up the template environment with the templates directory | |
| # env = Environment(loader=FileSystemLoader(proj_dir / 'templates')) | |
| # # Load the templates directly from the environment | |
| # template = env.get_template('template.j2') | |
| # template_html = env.get_template('template_html.j2') | |
| # def system_instructions(question_difficulty, topic, documents_str): | |
| # return f"""<s> [INST] You are a great teacher and your task is to create 10 questions with 4 choices with {question_difficulty} difficulty about the topic request "{topic}" only from the below given documents, {documents_str}. Then create answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". example is 'A10':'Q10:C3' [/INST]""" | |
| # # RA | |
| # RAG_db = gr.State() | |
| # def json_to_excel(output_json): | |
| # # Initialize list for DataFrame | |
| # data = [] | |
| # gr.Warning('Generating Shareable file link..',duration=30) | |
| # for i in range(1, 11): # Assuming there are 10 questions | |
| # question_key = f"Q{i}" | |
| # answer_key = f"A{i}" | |
| # question = output_json.get(question_key, '') | |
| # correct_answer_key = output_json.get(answer_key, '') | |
| # correct_answer = correct_answer_key.split(':')[-1] if correct_answer_key else '' | |
| # # Extract options | |
| # option_keys = [f"{question_key}:C{i}" for i in range(1, 6)] | |
| # options = [output_json.get(key, '') for key in option_keys] | |
| # # Add data row | |
| # data.append([ | |
| # question, # Question Text | |
| # "Multiple Choice", # Question Type | |
| # options[0], # Option 1 | |
| # options[1], # Option 2 | |
| # options[2] if len(options) > 2 else '', # Option 3 | |
| # options[3] if len(options) > 3 else '', # Option 4 | |
| # options[4] if len(options) > 4 else '', # Option 5 | |
| # correct_answer, # Correct Answer | |
| # 30, # Time in seconds | |
| # '' # Image Link | |
| # ]) | |
| # # Create DataFrame | |
| # df = pd.DataFrame(data, columns=[ | |
| # "Question Text", | |
| # "Question Type", | |
| # "Option 1", | |
| # "Option 2", | |
| # "Option 3", | |
| # "Option 4", | |
| # "Option 5", | |
| # "Correct Answer", | |
| # "Time in seconds", | |
| # "Image Link" | |
| # ]) | |
| # # Create a temporary file and save the DataFrame to it | |
| # temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx") | |
| # df.to_excel(temp_file.name, index=False) | |
| # # # Save to Excel file | |
| # # excel_path = proj_dir / "quiz_questions.xlsx" | |
| # # df.to_excel(str(excel_path), index=False) | |
| # return temp_file.name | |
| # with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green"), css="style.css") as QUIZBOT: | |
| # def load_model(): | |
| # RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0") | |
| # RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index') | |
| # return 'Ready to Go!!' | |
| # with gr.Column(scale=4): | |
| # gr.HTML(""" | |
| # <center> | |
| # <h1><span style="color: purple;">ADWITIYA</span> Customs Manual Quizbot</h1> | |
| # <h2>Generative AI-powered Capacity building for Training Officers</h2> | |
| # <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i> | |
| # </center> | |
| # """) | |
| # with gr.Column(scale=2): | |
| # load_btn = gr.Button("Click to Load!🚀") | |
| # load_text = gr.Textbox() | |
| # load_btn.click(load_model, [], load_text) | |
| # topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual") | |
| # with gr.Row(): | |
| # radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?") | |
| # generate_quiz_btn = gr.Button("Generate Quiz!🚀") | |
| # quiz_msg = gr.Textbox() | |
| # question_radios = [gr.Radio(visible=False) for _ in range(10)] | |
| # #@gr.dependencies.GPU | |
| # @generate_quiz_btn.click(inputs=[radio, topic], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")], api_name="generate_quiz") | |
| # def generate_quiz(question_difficulty, topic): | |
| # top_k_rank = 10 | |
| # RAG_db_ = RAG_db.value | |
| # documents_full = RAG_db_.search(topic, k=top_k_rank) | |
| # gr.Warning('Generation of Quiz may take 1 to 2 minutes. Please wait.',duration=60) | |
| # question_radio_list = [] | |
| # excel_file = None | |
| # count = 0 | |
| # while count <= 3: | |
| # try: | |
| # documents = [item['content'] for item in documents_full] | |
| # document_summaries = [f"[DOCUMENT {i + 1}]: {summary}{count}" for i, summary in enumerate(documents)] | |
| # documents_str = '\n'.join(document_summaries) | |
| # formatted_prompt = system_instructions(question_difficulty, topic, documents_str) | |
| # print(formatted_prompt) | |
| # # Use Qwen Client for quiz generation | |
| # response = client.predict( | |
| # query=formatted_prompt, | |
| # history=[], | |
| # system="You are a helpful assistant.", | |
| # api_name="/model_chat" | |
| # ) | |
| # print(response) | |
| # response1 = response[1][0][1] | |
| # # Find the first and last curly braces | |
| # start_index = response1.find('{') | |
| # end_index = response1.rfind('}') | |
| # # Extract only the JSON part | |
| # if start_index != -1 and end_index != -1: | |
| # cleaned_response = response1[start_index:end_index + 1] | |
| # # Try parsing the cleaned JSON | |
| # try: | |
| # output_json = json.loads(cleaned_response) | |
| # print('Parsed JSON:', output_json) | |
| # global quiz_data | |
| # quiz_data = output_json | |
| # # Generate the Excel file | |
| # excel_file = json_to_excel(output_json) | |
| # for question_num in range(1, 11): | |
| # question_key = f"Q{question_num}" | |
| # answer_key = f"A{question_num}" | |
| # question = quiz_data.get(question_key) | |
| # answer = quiz_data.get(quiz_data.get(answer_key)) | |
| # if not question or not answer: | |
| # continue | |
| # choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)] | |
| # choice_list = [quiz_data.get(choice_key, "Choice not found") for choice_key in choice_keys] | |
| # radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True) | |
| # question_radio_list.append(radio) | |
| # print('question_radio_list', question_radio_list) | |
| # if len(question_radio_list) == 10: | |
| # break | |
| # else: | |
| # print('10 questions not generated. Trying again!') | |
| # count += 1 | |
| # continue | |
| # except json.JSONDecodeError as e: | |
| # print(f"Failed to decode JSON: {e}") | |
| # else: | |
| # print("No valid JSON found in the response.") | |
| # except Exception as e: | |
| # count += 1 | |
| # print(f"Exception occurred: {e}") | |
| # if count == 3: | |
| # print('Retry exhausted') | |
| # gr.Warning('Sorry. Please try with another topic!') | |
| # else: | |
| # print(f"Trying again.. {count} time... please wait") | |
| # continue | |
| # return ['Quiz Generated!'] + question_radio_list + [excel_file] | |
| # check_button = gr.Button("Check Score") | |
| # score_textbox = gr.Markdown() | |
| # @check_button.click(inputs=question_radios, outputs=score_textbox) | |
| # def compare_answers(*user_answers): | |
| # user_answer_list = list(user_answers) | |
| # answers_list = [] | |
| # for question_num in range(1, 20): | |
| # answer_key = f"A{question_num}" | |
| # answer = quiz_data.get(quiz_data.get(answer_key)) | |
| # if not answer: | |
| # break | |
| # answers_list.append(answer) | |
| # score = sum(1 for item in user_answer_list if item in answers_list) | |
| # if score > 7: | |
| # message = f"### Excellent! You got {score} out of 10!" | |
| # elif score > 5: | |
| # message = f"### Good! You got {score} out of 10!" | |
| # else: | |
| # message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!" | |
| # return message | |
| # QUIZBOT.queue() | |
| # QUIZBOT.launch(debug=True) | |
| #????????????????????????????????? | |
| import pandas as pd | |
| import json | |
| import gradio as gr | |
| from pathlib import Path | |
| from ragatouille import RAGPretrainedModel | |
| from gradio_client import Client | |
| from tempfile import NamedTemporaryFile | |
| from sentence_transformers import CrossEncoder | |
| import numpy as np | |
| from time import perf_counter | |
| VECTOR_COLUMN_NAME = "vector" | |
| TEXT_COLUMN_NAME = "text" | |
| proj_dir = Path.cwd() | |
| # Set up logging | |
| import logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Replace Mixtral client with Qwen Client | |
| client = Client("Qwen/Qwen1.5-110B-Chat-demo") | |
| def system_instructions(question_difficulty, topic, documents_str): | |
| return f"""<s> [INST] You are a great teacher and your task is to create 10 questions with 4 choices with {question_difficulty} difficulty about the topic request "{topic}" only from the below given documents, {documents_str}. Then create answers. Index in JSON format, the questions as "Q#":"" to "Q#":"", the four choices as "Q#:C1":"" to "Q#:C4":"", and the answers as "A#":"Q#:C#" to "A#":"Q#:C#". Example: 'A10':'Q10:C3' [/INST]""" | |
| # RA | |
| RAG_db = gr.State() | |
| def json_to_excel(output_json): | |
| # Initialize list for DataFrame | |
| data = [] | |
| gr.Warning('Generating Shareable file link..', duration=30) | |
| for i in range(1, 11): # Assuming there are 10 questions | |
| question_key = f"Q{i}" | |
| answer_key = f"A{i}" | |
| question = output_json.get(question_key, '') | |
| correct_answer_key = output_json.get(answer_key, '') | |
| correct_answer = correct_answer_key.split(':')[-1] if correct_answer_key else '' | |
| # Extract options | |
| option_keys = [f"{question_key}:C{i}" for i in range(1, 6)] | |
| options = [output_json.get(key, '') for key in option_keys] | |
| # Add data row | |
| data.append([ | |
| question, # Question Text | |
| "Multiple Choice", # Question Type | |
| options[0], # Option 1 | |
| options[1], # Option 2 | |
| options[2] if len(options) > 2 else '', # Option 3 | |
| options[3] if len(options) > 3 else '', # Option 4 | |
| options[4] if len(options) > 4 else '', # Option 5 | |
| correct_answer, # Correct Answer | |
| 30, # Time in seconds | |
| '' # Image Link | |
| ]) | |
| # Create DataFrame | |
| df = pd.DataFrame(data, columns=[ | |
| "Question Text", | |
| "Question Type", | |
| "Option 1", | |
| "Option 2", | |
| "Option 3", | |
| "Option 4", | |
| "Option 5", | |
| "Correct Answer", | |
| "Time in seconds", | |
| "Image Link" | |
| ]) | |
| temp_file = NamedTemporaryFile(delete=False, suffix=".xlsx") | |
| df.to_excel(temp_file.name, index=False) | |
| return temp_file.name | |
| with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green")) as QUIZBOT: | |
| with gr.Column(scale=4): | |
| # gr.HTML(""" | |
| # <center> | |
| # <h1><span style="color: purple;">ADWITIYA</span> Customs Manual Quizbot</h1> | |
| # <h2>Generative AI-powered Capacity building for Training Officers</h2> | |
| # <i>⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️</i> | |
| # </center> | |
| # """) | |
| topic = gr.Textbox(label="Enter the Topic for Quiz", placeholder="Write any topic/details from Customs Manual") | |
| with gr.Row(): | |
| difficulty_radio = gr.Radio(["easy", "average", "hard"], label="How difficult should the quiz be?") | |
| model_radio = gr.Radio(choices=['(FAST) MiniLM-L6v2', '(ACCURATE) BGE reranker', '(HIGH ACCURATE) ColBERT'], | |
| value='(ACCURATE) BGE reranker', label="Embeddings", | |
| info="First query to ColBERT may take a little time") | |
| generate_quiz_btn = gr.Button("Generate Quiz!🚀") | |
| quiz_msg = gr.Textbox() | |
| question_radios = [gr.Radio(visible=False) for _ in range(10)] | |
| def generate_quiz(question_difficulty, topic, cross_encoder): | |
| top_k_rank = 10 | |
| documents = [] | |
| gr.Warning('Generating Quiz may take 1-2 minutes. Please wait.', duration=60) | |
| if cross_encoder == '(HIGH ACCURATE) ColBERT': | |
| gr.Warning('Retrieving using ColBERT.. First-time query will take a minute for model to load.. please wait') | |
| RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0") | |
| RAG_db.value = RAG.from_index('.ragatouille/colbert/indexes/cbseclass10index') | |
| documents_full = RAG_db.value.search(topic, k=top_k_rank) | |
| documents = [item['content'] for item in documents_full] | |
| else: | |
| document_start = perf_counter() | |
| query_vec = retriever.encode(topic) | |
| doc1 = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank) | |
| documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list() | |
| documents = [doc[TEXT_COLUMN_NAME] for doc in documents] | |
| query_doc_pair = [[topic, doc] for doc in documents] | |
| if cross_encoder == '(FAST) MiniLM-L6v2': | |
| cross_encoder1 = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') | |
| elif cross_encoder == '(ACCURATE) BGE reranker': | |
| cross_encoder1 = CrossEncoder('BAAI/bge-reranker-base') | |
| cross_scores = cross_encoder1.predict(query_doc_pair) | |
| sim_scores_argsort = list(reversed(np.argsort(cross_scores))) | |
| documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]] | |
| formatted_prompt = system_instructions(question_difficulty, topic, '\n'.join(documents)) | |
| try: | |
| response = client.predict(query=formatted_prompt, history=[], system="You are a helpful assistant.", api_name="/model_chat") | |
| response1 = response[1][0][1] | |
| # Extract JSON | |
| start_index = response1.find('{') | |
| end_index = response1.rfind('}') | |
| cleaned_response = response1[start_index:end_index + 1] if start_index != -1 and end_index != -1 else '' | |
| output_json = json.loads(cleaned_response) | |
| # Generate the Excel file | |
| excel_file = json_to_excel(output_json) | |
| question_radio_list = [] | |
| for question_num in range(1, 11): | |
| question_key = f"Q{question_num}" | |
| answer_key = f"A{question_num}" | |
| question = output_json.get(question_key) | |
| answer = output_json.get(output_json.get(answer_key)) | |
| if not question or not answer: | |
| continue | |
| choice_keys = [f"{question_key}:C{i}" for i in range(1, 5)] | |
| choice_list = [output_json.get(choice_key, "Choice not found") for choice_key in choice_keys] | |
| radio = gr.Radio(choices=choice_list, label=question, visible=True, interactive=True) | |
| question_radio_list.append(radio) | |
| return ['Quiz Generated!'] + question_radio_list + [excel_file] | |
| except json.JSONDecodeError as e: | |
| print(f"Failed to decode JSON: {e}") | |
| check_button = gr.Button("Check Score") | |
| score_textbox = gr.Markdown() | |
| def compare_answers(*user_answers): | |
| user_answer_list = list(user_answers) | |
| answers_list = [] | |
| for question_num in range(1, 20): | |
| answer_key = f"A{question_num}" | |
| answer = quiz_data.get(quiz_data.get(answer_key)) | |
| if not answer: | |
| break | |
| answers_list.append(answer) | |
| score = sum(1 for item in user_answer_list if item in answers_list) | |
| if score > 7: | |
| message = f"### Excellent! You got {score} out of 10!" | |
| elif score > 5: | |
| message = f"### Good! You got {score} out of 10!" | |
| else: | |
| message = f"### You got {score} out of 10! Don't worry. You can prepare well and try better next time!" | |
| return message | |
| QUIZBOT.queue() | |
| QUIZBOT.launch(debug=True) | |