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import streamlit as st
import pandas as pd
import numpy as np
import tempfile
import os
from dejan.veczip import veczip
import csv
import ast
def is_numeric(s):
"""Checks if a given string is numeric."""
try:
float(s)
return True
except:
return False
def parse_as_array(val):
"""Parses a string as an array of numbers."""
if isinstance(val, (int, float)):
return [val]
val_str = str(val).strip()
if val_str.startswith("[") and val_str.endswith("]"):
try:
arr = ast.literal_eval(val_str)
if isinstance(arr, list) and all(is_numeric(str(x)) for x in arr):
return arr
return None
except:
return None
parts = val_str.split(",")
if len(parts) > 1 and all(is_numeric(p.strip()) for p in parts):
return [float(p.strip()) for p in parts]
return None
def get_line_pattern(row):
"""Detects the pattern (text, number, or array) of a row."""
pattern = []
for val in row:
arr = parse_as_array(val)
if arr is not None:
pattern.append('arr')
else:
if is_numeric(val):
pattern.append('num')
else:
pattern.append('text')
return pattern
def detect_header(lines):
"""Detects if a CSV has a header."""
if len(lines) < 2:
return False
first_line_pattern = get_line_pattern(lines[0])
subsequent_patterns = [get_line_pattern(r) for r in lines[1:]]
if len(subsequent_patterns) > 1:
if all(p == subsequent_patterns[0] for p in subsequent_patterns) and first_line_pattern != subsequent_patterns[0]:
return True
else:
if subsequent_patterns and first_line_pattern != subsequent_patterns[0]:
return True
return False
def looks_like_id_column(col_values):
"""Checks if a column looks like an ID column (sequential integers)."""
try:
nums = [int(float(v)) for v in col_values]
return nums == list(range(nums[0], nums[0] + len(nums)))
except:
return False
def detect_columns(file_path):
"""Detects embedding and metadata columns in a CSV file."""
with open(file_path, "r", newline="", encoding="utf-8") as f:
try:
sample = f.read(1024*10) # Read a larger sample for sniffing
dialect = csv.Sniffer().sniff(sample, delimiters=[',','\t',';','|'])
delimiter = dialect.delimiter
except:
delimiter = ','
f.seek(0) # reset file pointer
reader = csv.reader(f, delimiter=delimiter)
first_lines = list(reader)[:10]
if not first_lines:
raise ValueError("No data")
has_header = detect_header(first_lines)
if has_header:
header = first_lines[0]
data = first_lines[1:]
else:
header = []
data = first_lines
if not data:
return has_header, [], [], delimiter
cols = list(zip(*data))
candidate_arrays = []
candidate_numeric = []
id_like_columns = set()
text_like_columns = set()
for ci, col in enumerate(cols):
col = list(col)
parsed_rows = [parse_as_array(val) for val in col]
if all(r is not None for r in parsed_rows):
lengths = {len(r) for r in parsed_rows}
if len(lengths) == 1:
candidate_arrays.append(ci)
continue
else:
text_like_columns.add(ci)
continue
if all(is_numeric(v) for v in col):
if looks_like_id_column(col):
id_like_columns.add(ci)
else:
candidate_numeric.append(ci)
else:
text_like_columns.add(ci)
identified_embedding_columns = set(candidate_arrays)
identified_metadata_columns = set()
if candidate_arrays:
identified_metadata_columns.update(candidate_numeric)
else:
if len(candidate_numeric) > 1:
identified_embedding_columns.update(candidate_numeric)
else:
identified_metadata_columns.update(candidate_numeric)
identified_metadata_columns.update(id_like_columns)
identified_metadata_columns.update(text_like_columns)
if header:
for ci, col_name in enumerate(header):
if col_name.lower() == 'id':
if ci in identified_embedding_columns:
identified_embedding_columns.remove(ci)
identified_metadata_columns.add(ci)
break
emb_cols = [header[i] if header and i < len(header) else i for i in identified_embedding_columns]
meta_cols = [header[i] if header and i < len(header) else i for i in identified_metadata_columns]
return has_header, emb_cols, meta_cols, delimiter
def load_and_validate_embeddings(input_file, target_dims):
"""Loads, validates, and summarizes embedding data from a CSV."""
print(f"Loading data from {input_file}...")
has_header, embedding_columns, metadata_columns, delimiter = detect_columns(input_file)
data = pd.read_csv(input_file, header=0 if has_header else None, delimiter=delimiter)
def is_valid_row(row):
for col in embedding_columns:
if parse_as_array(row[col]) is None:
return False
return True
valid_rows_filter = data.apply(is_valid_row, axis=1)
data = data[valid_rows_filter]
print("\n=== File Summary ===")
print(f"File: {input_file}")
print(f"Rows: {len(data)}")
print(f"Metadata Columns: {metadata_columns}")
print(f"Embedding Columns: {embedding_columns}")
print("====================\n")
return data, embedding_columns, metadata_columns, has_header, list(data.columns)
def save_compressed_embeddings(output_file, metadata, compressed_embeddings, embedding_columns, original_columns, has_header):
"""Saves compressed embeddings to a CSV file."""
print(f"Saving compressed data to {output_file}...")
metadata = metadata.copy()
for i, col in enumerate(embedding_columns):
metadata[col] = [compressed_embeddings[i][j].tolist() for j in range(compressed_embeddings[i].shape[0])]
header_option = True if has_header else False
final_df = metadata.reindex(columns=original_columns) if original_columns else metadata
final_df.to_csv(output_file, index=False, header=header_option)
print(f"Data saved to {output_file}.")
def run_veczip(input_file, target_dims=16):
"""Runs veczip compression on the input data."""
data, embedding_columns, metadata_columns, has_header, original_columns = load_and_validate_embeddings(input_file, target_dims)
all_embeddings = []
for col in embedding_columns:
embeddings = np.array([parse_as_array(x) for x in data[col].values])
all_embeddings.append(embeddings)
combined_embeddings = np.concatenate(all_embeddings, axis=0)
compressor = veczip(target_dims=target_dims)
retained_indices = compressor.compress(combined_embeddings)
compressed_embeddings = []
for embeddings in all_embeddings:
compressed_embeddings.append(embeddings[:, retained_indices])
temp_output = tempfile.NamedTemporaryFile(suffix='.csv', delete=False)
save_compressed_embeddings(temp_output.name, data[metadata_columns], compressed_embeddings, embedding_columns, original_columns, has_header)
return temp_output.name
# Streamlit App
def main():
st.title("Veczip Embeddings Compressor")
uploaded_file = st.file_uploader("Upload CSV file with embeddings", type=["csv"])
if uploaded_file:
try:
with st.spinner("Analyzing and compressing embeddings..."):
temp_file = tempfile.NamedTemporaryFile(delete=False)
temp_file.write(uploaded_file.read())
temp_file.close()
output_file_path = run_veczip(temp_file.name)
with open(output_file_path, 'rb') as f:
st.download_button(
label="Download Compressed CSV",
data=f,
file_name="compressed_embeddings.csv",
mime="text/csv"
)
os.unlink(temp_file.name)
os.unlink(output_file_path)
st.success("Compression complete! Download your compressed file below.")
except Exception as e:
st.error(f"Error processing file: {e}")
if __name__ == "__main__":
main() |