Delete main.py

main.py

@@ -1,33 +0,0 @@
-import argparse
-import pandas as pd
-import streamlit as st
-from generate_schema import generate_schema
-from fetch_data import fetch_real_data
-from synthetic_generator import train_and_generate_synthetic
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--prompt", type=str, required=True, help="Describe the dataset you want")
-    parser.add_argument("--domain", type=str, default="healthcare", help="Domain to fetch real data from (optional)")
-    args = parser.parse_args()
-
-    # Retrieve API token from Streamlit secrets
-    hf_token = st.secrets["hf_token"]
-
-    # Step 1: Generate schema using LLM
-    schema = generate_schema(args.prompt, hf_token)
-    print(f"📊 Generated schema: {schema}")
-
-    # Step 2: Fetch real data (optional)
-    real_data = fetch_real_data(args.domain)
-
-    # Step 3: Preprocess (if necessary)
-    real_data = real_data[schema['columns']]  # Match columns from schema
-    print(f"✅ Fetched real data with shape: {real_data.shape}")
-
-    # Step 4: Train GAN and generate synthetic data
-    output_path = f"outputs/synthetic_{args.domain}.csv"
-    train_and_generate_synthetic(real_data, schema, output_path)
-
-if __name__ == "__main__":
-    main()