Spaces:
Runtime error
Runtime error
File size: 3,708 Bytes
d1430bc 1cc5040 d1430bc 48a3fbf d1430bc 1cc5040 d1430bc 31df1ec d1430bc c270693 d1430bc c075310 d1430bc 1cc5040 d1430bc 1cc5040 d1430bc 1cc5040 d1430bc 1cc5040 d1430bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
import os
import re
from dataclasses import asdict
import pandas as pd
from langchain.callbacks import get_openai_callback
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import DataFrameLoader
from langchain.prompts import PromptTemplate
from langchain.text_splitter import TokenTextSplitter
from tqdm import tqdm
from wandb.integration.langchain import WandbTracer
import wandb
from config import config
def get_data(artifact_name: str, total_episodes: int = None):
podcast_artifact = wandb.use_artifact(artifact_name, type="dataset")
podcast_artifact_dir = podcast_artifact.download(config.root_artifact_dir)
filename = artifact_name.split(":")[0].split("/")[-1]
df = pd.read_csv(os.path.join(podcast_artifact_dir, f"{filename}.csv"))
if total_episodes is not None:
df = df.iloc[:total_episodes]
return df
def extract_questions(episode_df: pd.DataFrame):
# load docs into langchain format
loader = DataFrameLoader(episode_df, page_content_column="transcript")
data = loader.load()
# split the documents
text_splitter = TokenTextSplitter.from_tiktoken_encoder(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(data)
print(f"Number of documents for podcast {data[0].metadata['title']}: {len(docs)}")
# initialize LLM
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
# define prompt
prompt = """You are provided with a short transcript from a podcast episode.
Your task is to extract the relevant and most important questions one might ask from the transcript and present them in a bullet-point list.
Ensure that the total number of questions is no more than 3.
TRANSCRIPT:
{text}
QUESTIONS:"""
prompt_template = PromptTemplate(template=prompt, input_variables=["text"])
pattern = r"\d+\.\s"
que_by_llm = []
for doc in docs:
llm_chain = LLMChain(llm=llm, prompt=prompt_template)
out = llm_chain.run(doc)
cleaned_ques = re.sub(pattern, "", out).split("\n")
que_by_llm.extend(cleaned_ques)
return que_by_llm
if __name__ == "__main__":
# initialize wandb tracer
WandbTracer.init(
{
"project": config.project_name,
"job_type": "extract_questions",
"config": asdict(config),
}
)
# get data
df = get_data(artifact_name=config.summarized_data_artifact)
questions = []
with get_openai_callback() as cb:
for episode in tqdm(
df.iterrows(), total=len(df), desc="Extracting questions from episodes"
):
episode_data = episode[1].to_frame().T
episode_questions = extract_questions(episode_data)
questions.append(episode_questions)
print("*" * 25)
print(cb)
print("*" * 25)
wandb.log(
{
"total_prompt_tokens": cb.prompt_tokens,
"total_completion_tokens": cb.completion_tokens,
"total_tokens": cb.total_tokens,
"total_cost": cb.total_cost,
}
)
df["questions"] = questions
# log to wandb artifact
path_to_save = os.path.join(config.root_data_dir, "summarized_que_podcasts.csv")
df.to_csv(path_to_save, index=False)
artifact = wandb.Artifact("summarized_que_podcasts", type="dataset")
artifact.add_file(path_to_save)
wandb.log_artifact(artifact)
# create wandb table
df["questions"] = df["questions"].apply(lambda x: "\n".join(x))
table = wandb.Table(dataframe=df)
wandb.log({"summarized_que_podcasts": table})
WandbTracer.finish()
|