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| import os | |
| import re | |
| from ast import literal_eval | |
| import wandb | |
| import gradio as gr | |
| import pandas as pd | |
| from langchain.callbacks import get_openai_callback | |
| from langchain.chains import RetrievalQA | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langchain.vectorstores import Chroma | |
| from src.config import config | |
| # download and read data | |
| api = wandb.Api() | |
| artifact_df = api.artifact(config.summarized_que_data_artifact) | |
| artifact_df.download(config.root_data_dir) | |
| artifact_embeddings = api.artifact(config.transcript_embeddings_artifact) | |
| chromadb_dir = artifact_embeddings.download(config.root_data_dir / "chromadb") | |
| df_path = config.root_data_dir / "summarized_que_podcasts.csv" | |
| df = pd.read_csv(df_path) | |
| def embed_video(title: str): | |
| video_url = df[df["title"] == title]["url"].values[0] | |
| match = re.search(r"v=([-\w]+)", video_url) | |
| video_id = match.group(1) | |
| # embed video | |
| # video_embed = f"<iframe width='600' height='330' src=https://www.youtube.com/embed/{video_id} frameborder='0' allowfullscreen></iframe>" | |
| video_embed = f"<iframe width='580' height='360' src=https://www.youtube.com/embed/{video_id} frameborder='0' allowfullscreen style='width:100%; max-width:100%;'></iframe>" | |
| return video_embed | |
| def get_podcast_info(title: str): | |
| # get questions | |
| questions = df[df["title"] == title]["questions"].values[0] | |
| questions = literal_eval(questions) | |
| que_str = "" | |
| for que in questions: | |
| que_str += f"👉 {que}\n" | |
| # get summary | |
| summary = df[df["title"] == title]["summary"].values[0] | |
| return summary, que_str | |
| def get_answer(podcast: str, question: str): | |
| index = df[df["title"] == podcast].index[0] | |
| db_dir = os.path.join(chromadb_dir, str(index)) | |
| embeddings = OpenAIEmbeddings() | |
| db = Chroma(persist_directory=db_dir, embedding_function=embeddings) | |
| prompt_template = """Use the following pieces of context to answer the question. | |
| If you don't know the answer, just say that you don't know, don't try to make up an answer. | |
| Don't add your opinions or interpretations. Ensure that you complete the answer. | |
| If the question is not relevant to the context, just say that it is not relevant. | |
| CONTEXT: | |
| {context} | |
| QUESTION: {question} | |
| ANSWER:""" | |
| prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"]) | |
| retriever = db.as_retriever() | |
| retriever.search_kwargs["k"] = 2 | |
| qa = RetrievalQA.from_chain_type( | |
| llm=ChatOpenAI(temperature=0), | |
| chain_type="stuff", | |
| retriever=retriever, | |
| chain_type_kwargs={"prompt": prompt}, | |
| return_source_documents=True, | |
| ) | |
| with get_openai_callback() as cb: | |
| result = qa({"query": question}) | |
| print(cb) | |
| answer = result["result"] | |
| return answer | |
| with gr.Blocks() as demo: | |
| gr.Markdown("<h1><center>Welcome to Gradient Dissent QA Bot 🤖</center></h1>") | |
| gr.Markdown( | |
| "#### The purpose of this QA bot is to provide answers to questions related to podcast episodes from Weights & Biases' [Gradient Dissent Podcast](https://www.youtube.com/playlist?list=PLD80i8An1OEEb1jP0sjEyiLG8ULRXFob_)." | |
| ) | |
| gr.Markdown( | |
| "#### First select a podcast episode and click `Get Podcast Info` to get the summary and possible questions about the podcast episode." | |
| ) | |
| gr.Markdown( | |
| "#### Then ask a question about the podcast episode and click `Get Answer` to get the answer." | |
| ) | |
| gr.Markdown( | |
| "#### Read the report for understanding how I built this QA bot [here](https://wandb.ai/gladiator/gradient_dissent_qabot/reports/Building-a-Q-A-Bot-for-Weights-Biases-Gradient-Dissent-Podcast--Vmlldzo0MTcyMDQz)" | |
| ) | |
| gr.Markdown( | |
| "#### GitHub Repo [here](https://github.com/Gladiator07/wandb-gradient-dissent-bot/tree/main)" | |
| ) | |
| gr.Markdown("<br>") | |
| with gr.Row(): | |
| with gr.Column(scale=0.5): | |
| dropdown = gr.Dropdown( | |
| df["title"].to_list(), label="Select a Podcast Episode", value=df.iloc[0]["title"] | |
| ) | |
| podcast_info_btn = gr.Button("Get Podcast Info") | |
| podcast_info_btn.click( | |
| fn=embed_video, | |
| inputs=dropdown, | |
| outputs=gr.HTML(label="Podcast Video"), | |
| ) | |
| question_box = gr.Textbox(label="Ask a question about the podcast episode") | |
| with gr.Row(): | |
| ques_clear_btn = gr.Button("Clear") | |
| ques_btn = gr.Button("Get Answer") | |
| ques_btn.click( | |
| fn=get_answer, | |
| inputs=[dropdown, question_box], | |
| outputs=gr.Textbox(label="Answer"), | |
| ) | |
| ques_clear_btn.click(lambda: None, None, question_box, queue=False) | |
| with gr.Column(scale=0.5): | |
| podcast_info_btn.click( | |
| fn=get_podcast_info, | |
| inputs=dropdown, | |
| outputs=[ | |
| gr.Text(label="Summary of the podcast"), | |
| gr.Text(label="Some of the questions you can ask"), | |
| ], | |
| ) | |
| demo.launch() | |