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import os
import numpy as np
import pandas as pd
from datasets import Dataset, DownloadMode, load_dataset
from gradio_client import Client
from src.my_logger import setup_logger
SUBREDDIT = os.environ["SUBREDDIT"]
USERNAME = os.environ["USERNAME"]
OG_DATASET = f"{USERNAME}/dataset-creator-reddit-{SUBREDDIT}"
PROCESSED_DATASET = os.environ['PROCESSED_DATASET']
client = Client("derek-thomas/nomic-embeddings")
logger = setup_logger(__name__)
async def load_datasets():
# Get latest datasets locally
logger.debug(f"Trying to download {PROCESSED_DATASET}")
dataset = load_dataset(PROCESSED_DATASET, download_mode=DownloadMode.FORCE_REDOWNLOAD)
logger.debug(f"Loaded {PROCESSED_DATASET}")
logger.debug(f"Trying to download {OG_DATASET}")
original_dataset = load_dataset(OG_DATASET, download_mode=DownloadMode.FORCE_REDOWNLOAD)
logger.debug(f"Loaded {OG_DATASET}")
return dataset, original_dataset
def merge_and_update_datasets(dataset, original_dataset):
# Merge and figure out which rows need to be updated with embeddings
odf = original_dataset['train'].to_pandas()
df = dataset['train'].to_pandas()
# Step 1: Merge df onto odf
# We'll bring in 'content' and 'embedding' from df to compare and possibly update 'embedding'
merged_df = pd.merge(odf, df[['id', 'content', 'embedding']], on='id', how='left', suffixes=('_odf', ''))
updated_rows = len(merged_df[merged_df.content != merged_df.content_odf])
# Step 2: Compare 'content' from odf and df, update 'embedding' if they differ
merged_df['embedding'] = np.where(merged_df['content_odf'] != merged_df['content'], None, merged_df['embedding'])
# Step 3: Cleanup - keep only the necessary columns.
# Assuming you want to keep 'content' from 'odf' and the updated 'embedding', and drop the rest
merged_df = merged_df.drop(columns=['content', 'new', 'updated']) # Update columns to match df
merged_df.rename(columns={'content_odf': 'content'}, inplace=True) # Rename 'content_odf' back to 'content'
logger.info(f"Updating {updated_rows} rows...")
# Iterate over the DataFrame rows where 'embedding' is None
for index, row in merged_df[merged_df['embedding'].isnull()].iterrows():
# Update 'embedding' for the current row using our function
merged_df.at[index, 'embedding'] = update_embeddings(row['content'])
dataset['train'] = Dataset.from_pandas(merged_df)
logger.info(f"Updated {updated_rows} rows")
return dataset
def update_embeddings(content):
embedding = client.predict(content, api_name="/embed")
return np.array(embedding)
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