app.py

"""
Credit to Derek Thomas, derek@huggingface.co
"""

import subprocess

# subprocess.run(["pip", "install", "--upgrade", "transformers[torch,sentencepiece]==4.34.1"])
import spaces
import logging
from pathlib import Path
from time import perf_counter

import gradio as gr
from jinja2 import Environment, FileSystemLoader
import numpy as np
from sentence_transformers import CrossEncoder

from backend.query_llm import generate_hf, generate_openai,generate_gemini
from backend.semantic_search import table, retriever

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__)

# 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')

# crossEncoder
#cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2') 
cross_encoder = CrossEncoder('BAAI/bge-reranker-base')
# Examples
examples = ['My transhipment cargo is missing','can u explain and tabulate difference between b 17 bond and a warehousing bond',
            'What are benefits of  the AEO Scheme and eligibility criteria?',
            'What are penalties for customs offences? ', 'what are penalties to customs officers misusing their powers under customs act?','What are eligibility criteria for exemption from cost recovery charges','list in detail what is procedure for obtaining new approval for openeing a CFS attached to an ICD']


def add_text(history, text):
    history = [] if history is None else history
    history = history + [(text, None)]
    print('add_text function done..returning history' ,history)
    return history, gr.Textbox(value="", interactive=False)


def bot(history, api_kind):
    top_rerank = 15
    top_k_rank = 10
    query = history[-1][0]
    print('history[-1][0]',history[-1][0])
    print('api kind ',api_kind)

    if not query:
         gr.Warning("Please submit a non-empty string as a prompt")
         raise ValueError("Empty string was submitted")

    logger.warning('Retrieving documents...')
    # Retrieve documents relevant to query
    document_start = perf_counter()

    query_vec = retriever.encode(query)
    print(query)
    query_vec_flat = [arr.flatten() for arr in query_vec]
    logger.warning(f'Finished query vec')
    #documents = table.search(query_vec_flat, vector_column_name=VECTOR_COLUMN_NAME).limit(top_k_rank).to_list()



    logger.warning(f'Finished search')
    documents = table.search(query_vec, vector_column_name=VECTOR_COLUMN_NAME).limit(top_rerank).to_list()
    documents = [doc[TEXT_COLUMN_NAME] for doc in documents]
    logger.warning(f'start cross encoder {len(documents)}')
    # Retrieve documents relevant to query
    query_doc_pair = [[query, doc] for doc in documents]
    cross_scores = cross_encoder.predict(query_doc_pair)
    sim_scores_argsort = list(reversed(np.argsort(cross_scores)))
    logger.warning(f'Finished cross encoder {len(documents)}')
    
    documents = [documents[idx] for idx in sim_scores_argsort[:top_k_rank]]
    logger.warning(f'num documents {len(documents)}')

    document_time = perf_counter() - document_start
    logger.warning(f'Finished Retrieving documents in {round(document_time, 2)} seconds...')

    # Create Prompt
    prompt = template.render(documents=documents, query=query)
    prompt_html = template_html.render(documents=documents, query=query)

    if api_kind == "HuggingFace":
         generate_fn = generate_hf
    elif api_kind == "Gemini":
         print("Gemini condition satisfied")
         generate_fn = generate_gemini
    elif api_kind is None:
         gr.Warning("API name was not provided")
         raise ValueError("API name was not provided")
    else:
         gr.Warning(f"API {api_kind} is not supported")
         raise ValueError(f"API {api_kind} is not supported")
    try:
        count_tokens = lambda text: len([token.strip() for token in text.split() if token.strip()])
        print(prompt_html,'token count is',count_tokens(prompt_html))
        history[-1][1] = ""
        for character in generate_fn(prompt, history[:-1]):
            history[-1][1] = character
            yield history, prompt_html
        print('final history is ',history)
        # return history[-1][1], prompt_html
           
    except Exception as e:  # Catch any exception
        print('An unexpected error occurred during generation:', str(e))
        yield f"An unexpected error occurred during generation: {str(e)}"

with gr.Blocks(theme='WeixuanYuan/Soft_dark') as CHATBOT:
    # Beautiful heading with logo
    gr.HTML(value="""
    <div style="display: flex; align-items: center; justify-content: space-between;">
      <h1 style="color: #008000">ADWITIYA - <span style="color: #008000">Customs Manual Chatbot</span></h1>
      <img src='logo.png' alt="Chatbot" width="50" height="50" />
    </div>
    """, elem_id="heading")

    # Formatted description
    gr.HTML(value="""<p style="font-family: sans-serif; font-size: 16px;">A free chat bot developed by National Customs   		Targeting Center  using Open source LLMs.(Dedicated to 75th Batch IRS Probationers)</p>""", elem_id="description")
    
    chatbot = gr.Chatbot(
      [],
      elem_id="chatbot",
      avatar_images=('https://aui.atlassian.com/aui/8.8/docs/images/avatar-person.svg',
                      'https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg'),
      bubble_full_width=False,
      show_copy_button=True,
      show_share_button=True,
      )
    
    with gr.Row():
        txt = gr.Textbox(
                scale=3,
                show_label=False,
                placeholder="Enter text and press enter",
                container=False,
                )
        txt_btn = gr.Button(value="Submit text", scale=1)

    api_kind = gr.Radio(choices=["HuggingFace","Gemini"], value="HuggingFace")

    prompt_html = gr.HTML()
    #prompt_html = gr.Textbox(label='Retrieved Documents')
    try:
        # Turn off interactivity while generating if you click
        txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
                bot, [chatbot, api_kind], [chatbot, prompt_html])
    except Exception as e:
        print ('Exception  txt btn click ' ,str(e))
    # Turn it back on
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
    try:
        # Turn off interactivity while generating if you hit enter
        txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
                bot, [chatbot, api_kind], [chatbot, prompt_html])
    except Exception as e:
        print ('Exception  ' ,str(e))

    # Turn it back on
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)

    # Examples
    gr.Examples(examples, txt)
    
    # QUIZBOT CODE
    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 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]"""

    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;">AI NANBAN</span> - CBSE Class Quiz Maker</h1>
      <h2>AI-powered Learning Game</h2>
      <i>⚠️ Students create quiz from any topic /CBSE Chapter ! ⚠️</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 from CBSE notes")

    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)
    
        

        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



demo = gr.TabbedInterface([CHATBOT,QUIZBOT], ["AI ChatBot", "AI Nanban-Quizbot"])


demo.queue()
demo.launch(debug=True)