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 from sentence_transformers import CrossEncoder from backend.semantic_search import table, retriever 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""" [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() quiz_data = None 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 '' correct_answer = correct_answer_key.split(':')[-1].replace('C', '').strip() 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 # Define a colorful theme colorful_theme = gr.themes.Default( primary_hue="cyan", # Set a bright cyan as primary color secondary_hue="yellow", # Set a bright magenta as secondary color neutral_hue="purple" # Optionally set a neutral color ) #with gr.Blocks(title="Quiz Maker", theme=gr.themes.Default(primary_hue="green", secondary_hue="green")) as QUIZBOT: with gr.Blocks(title="Quiz Maker", theme=colorful_theme) as QUIZBOT: # Create a single row for the HTML and Image with gr.Row(): with gr.Column(scale=2): gr.Image(value='logo.png', height=200, width=200) with gr.Column(scale=6): gr.HTML("""

ADWITIYA Customs Manual Quizbot

Generative AI-powered Capacity building for Training Officers

⚠️ NACIN Faculties create quiz from any topic dynamically for classroom evaluation after their sessions ! ⚠️
""") 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=[ '(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)] @generate_quiz_btn.click(inputs=[difficulty_radio, topic, model_radio], outputs=[quiz_msg] + question_radios + [gr.File(label="Download Excel")]) 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 2 minute for model to load.. please wait',duration=100) 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') if 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)) print(' Formatted Prompt : ' ,formatted_prompt) 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 '' print('Cleaned Response :',cleaned_response) output_json = json.loads(cleaned_response) # Assign the extracted JSON to quiz_data for use in the comparison function global quiz_data quiz_data = output_json # 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() @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)