app.py

import time
import gradio as gr
import logging
from langchain_community.document_loaders import OnlinePDFLoader
# from langchain.document_loaders import PDFMinerLoader,CSVLoader ,UnstructuredWordDocumentLoader,TextLoader,OnlinePDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain.docstore.document import Document
from youtube_transcript_api import YouTubeTranscriptApi
import chatops 

logger = logging.getLogger(__name__)

DEVICE = 'cpu'
MAX_NEW_TOKENS = 4096
DEFAULT_TEMPERATURE = 0.1
DEFAULT_MAX_NEW_TOKENS = 2048
MAX_INPUT_TOKEN_LENGTH = 4000
DEFAULT_CHAR_LENGTH = 1000

EXAMPLES = ["https://www.youtube.com/watch?v=aircAruvnKk&ab_channel=3Blue1Brown",
            "https://www.youtube.com/watch?v=Ilg3gGewQ5U",
            "https://www.youtube.com/watch?v=WUvTyaaNkzM"
            ]



def clear_chat():
    return []

def get_text_from_youtube_link(video_link,max_video_length=800):
    video_text = ""
    video_id = video_link.split("watch?v=")[1].split("&")[0]
    srt = YouTubeTranscriptApi.get_transcript(video_id)
    for text_data in srt:
        video_text = video_text + " " + text_data.get("text")
    if len(video_text) > max_video_length:
        print(video_text)
        return video_text[0:max_video_length]
    else:
        print("SRT might be disabled for the video . Uunable to get SRT")
        return video_text

def process_documents(documents,data_chunk=1500,chunk_overlap=100):
    text_splitter = CharacterTextSplitter(chunk_size=data_chunk, chunk_overlap=chunk_overlap,separator='\n')
    texts = text_splitter.split_documents(documents)
    return texts

def process_youtube_link(link, document_name="youtube-content",char_length=1000):
    try:
        metadata = {"source": f"{document_name}.txt"}
        return [Document(page_content=get_text_from_youtube_link(video_link=link,max_video_length=char_length), metadata=metadata)]
    except Exception as err:
        logger.error(f'Error in reading document. {err}')


def create_prompt():
    prompt_template = """As a chatbot asnwer the questions regarding the content in the video. 
    Use the following context to answer. 
    If you don't know the answer, just say I don't know. 

    {context}

    Question: {question}
    Answer :"""
    prompt = PromptTemplate(
        template=prompt_template, input_variables=["context", "question"]
    )
    return prompt

def youtube_chat(youtube_link,API_key,llm='HuggingFace',temperature=0.1,max_tokens=1096,char_length=1500):
    
    document  = process_youtube_link(link=youtube_link,char_length=char_length)
    print("docuemt:",document)
    embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-base',model_kwargs={"device": DEVICE})
    texts = process_documents(documents=document)
    global vector_db
    vector_db = FAISS.from_documents(documents=texts, embedding= embedding_model)
    global qa

    if llm == 'HuggingFace':
        chat = chatops.get_hugging_face_model(
                            model_id="tiiuae/falcon-7b-instruct",
                            API_key=API_key,
                            temperature=temperature,
                            max_tokens=max_tokens
                            )
    else:
        chat = chatops.get_openai_chat_model(API_key=API_key)
    chain_type_kwargs = {"prompt": create_prompt()}

    qa = RetrievalQA.from_chain_type(llm=chat,
                                chain_type='stuff',
                                retriever=vector_db.as_retriever(),
                                chain_type_kwargs=chain_type_kwargs,
                                return_source_documents=True
                            )
    return "Youtube link Processing completed ..."

def infer(question, history):
    # res = []
    # # for human, ai in history[:-1]:
    # #     pair = (human, ai)
    # #     res.append(pair)
    
    # chat_history = res
    result = qa({"query": question})
    matching_docs_score = vector_db.similarity_search_with_score(question)
    
    return result["result"]

def bot(history):
    response = infer(history[-1][0], history)
    history[-1][1] = ""
    
    for character in response:     
        history[-1][1] += character
        time.sleep(0.05)
        yield history

def add_text(history, text):
    history = history + [(text, None)]
    return history, ""


css="""
#col-container {max-width: 2048px; margin-left: auto; margin-right: auto;}
"""

title = """
<div style="text-align: center;max-width: 2048px;">
    <h1>Chat with Youtube Videos </h1>
    <p style="text-align: center;">Upload a youtube link of any video-lecture/song/Research/Conference & ask Questions to chatbot with the tool.
    <i> Tools uses State of the Art Models from  HuggingFace/OpenAI so, make sure to add your key.</i>
    </p>
</div>
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column(elem_id="col-container"):
            gr.HTML(title)

    with gr.Column():
        with gr.Row():
            LLM_option = gr.Dropdown(['HuggingFace','OpenAI'],label='Select HuggingFace/OpenAI')
            API_key = gr.Textbox(label="Add API key", type="password",autofocus=True)

    with gr.Group():    
        chatbot = gr.Chatbot(height=270)
    
    with gr.Row():
        question = gr.Textbox(label="Type your question !",lines=1)
        with gr.Row():
            submit_btn = gr.Button(value="Send message", variant="primary", scale = 1)
            clean_chat_btn =  gr.Button("Delete Chat")

    with gr.Column():
            with gr.Row():
                youtube_link = gr.Textbox(label="Add your you tube Link",text_align='left',autofocus=True)
                with gr.Row():
                    load_youtube_bt = gr.Button("Process Youtube Link",)
                    langchain_status = gr.Textbox(label="Status", placeholder="", interactive = False)

            with gr.Column():
                with gr.Accordion(label='Advanced options', open=False):
                    max_new_tokens = gr.Slider(
                        label='Max new tokens',
                        minimum=2048,
                        maximum=MAX_NEW_TOKENS,
                        step=1,
                        value=DEFAULT_MAX_NEW_TOKENS,
                        )
                    temperature = gr.Slider(label='Temperature',minimum=0.1,maximum=4.0,step=0.1,value=DEFAULT_TEMPERATURE,)
                    char_length = gr.Slider(label='Max Character',
                        minimum= DEFAULT_CHAR_LENGTH,
                        maximum = 5*DEFAULT_CHAR_LENGTH,
                        step = 500,value= 1500
                    )
    
    load_youtube_bt.click(youtube_chat,inputs= [youtube_link,API_key,LLM_option,temperature,max_new_tokens,char_length],outputs=[langchain_status], queue=False)

    clean_chat_btn.click(clear_chat, [], chatbot)

    question.submit(add_text, inputs=[chatbot, question], outputs=[chatbot, question]).then(bot, chatbot, chatbot)
    submit_btn.click(add_text, inputs=[chatbot, question], outputs=[chatbot, question]).then(bot, chatbot, chatbot)

demo.launch()