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# importing all the necessary files
from IPython.display import YouTubeVideo
from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import LLMChain
from langchain.chains.summarize import load_summarize_chain
from langchain.llms import HuggingFacePipeline
from langchain import PromptTemplate
import locale
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
import langchain
print(langchain.__version__)
#Loading a sample video into transcript
loader = YoutubeLoader.from_youtube_url("https://www.youtube.com/watch?v=tAuRQs_d9F8&t=52s")
transcript = loader.load()
# Recursive splitting of text and storing it into texts
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=50)
texts = text_splitter.split_documents(transcript)
# Loading the model
model_repo = 'tiiuae/falcon-rw-1b'
tokenizer = AutoTokenizer.from_pretrained(model_repo)
model = AutoModelForCausalLM.from_pretrained(model_repo,
device_map='auto',
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
trust_remote_code=True
)
max_len = 2048 # 1024
task = "text-generation"
T = 0
# Building the pipeline
pipe = pipeline(
task=task,
model=model,
tokenizer=tokenizer,
max_length=max_len,
temperature=T,
top_p=0.95,
repetition_penalty=1.15,
pad_token_id = 11
)
llm = HuggingFacePipeline(pipeline=pipe, model_kwargs = {'temperature':0})
#Intitializing the LLM chain
template = """
Write a concise summary of the following text delimited by triple backquotes.
Return your response in bullet points which covers the key points of the text.
```{text}```
BULLET POINT SUMMARY:
"""
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
locale.getpreferredencoding = lambda: "UTF-8"
# import and intialize the question answer pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
text1 = """{}""".format(transcript[0])[14:]
context = text1
# Get the context of the video
def get_context(input_text):
loader = YoutubeLoader.from_youtube_url("{}".format(input_text))
transcript = loader.load()
texts = text_splitter.split_documents(transcript)
text1 = """{}""".format(transcript[0])[14:]
context = text1
return context
# Building the bot function
def build_the_bot(text1):
context = text1
return('Bot Build Successfull!!!')
# Building the bot summarizer function
def build_the_bot_summarizer(text1):
text = text1
return llm_chain.run(text)
# The chat space for gradio is servered here
def chat(chat_history, user_input, context):
output = question_answerer(question=user_input, context=context)
bot_response = output["answer"]
#print(bot_response)
response = ""
for letter in ''.join(bot_response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:
response += letter + ""
yield chat_history + [(user_input, response)]
# Serving the entre gradio app
with gr.Blocks() as demo:
gr.Markdown('# YouTube Q&A and Summarizer Bot')
with gr.Tab("Input URL of video you wanna load -"):
text_input = gr.Textbox()
text_output = gr.Textbox()
text_button1 = gr.Button("Build the Bot!!!")
text_button1.click(build_the_bot, get_context(text_input), text_output)
text_button2 = gr.Button("Summarize...")
text_button2.click(build_the_bot_summarizer, get_context(text_input), text_output)
with gr.Tab("Knowledge Base -"):
# inputbox = gr.Textbox("Input your text to build a Q&A Bot here.....")
chatbot = gr.Chatbot()
message = gr.Textbox ("What is this Youtube Video about?")
message.submit(chat, [chatbot, message], chatbot, get_context(text_input))
demo.queue().launch()
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