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import streamlit as st
import pandas as pd
import os
import json
import requests
from ctgan import CTGAN
from sklearn.preprocessing import LabelEncoder
def generate_schema(prompt):
"""Fetches schema from Hugging Face Spaces API."""
API_URL = "https://infinitymatter-Synthetic_Data_Generator_SRIJAN.hf.space/run/predict"
# Fetch API token securely
hf_token = st.secrets["hf_token"]
headers = {"Authorization": f"Bearer {hf_token}"}
payload = {"data": [prompt]}
try:
response = requests.post(API_URL, headers=headers, json=payload)
response.raise_for_status()
schema = response.json()
if 'columns' not in schema or 'types' not in schema or 'size' not in schema:
raise ValueError("Invalid schema format!")
return schema
except requests.exceptions.RequestException as e:
st.error(f"❌ API request failed: {e}")
return None
except json.JSONDecodeError:
st.error("❌ Failed to parse JSON response.")
return None
def train_and_generate_synthetic(real_data, schema, output_path):
"""Trains a CTGAN model and generates synthetic data."""
categorical_cols = [col for col, dtype in zip(schema['columns'], schema['types']) if dtype == 'string']
# Store label encoders
label_encoders = {}
for col in categorical_cols:
le = LabelEncoder()
real_data[col] = le.fit_transform(real_data[col])
label_encoders[col] = le
# Train CTGAN
gan = CTGAN(epochs=300)
gan.fit(real_data, categorical_cols)
# Generate synthetic data
synthetic_data = gan.sample(schema['size'])
# Decode categorical columns
for col in categorical_cols:
synthetic_data[col] = label_encoders[col].inverse_transform(synthetic_data[col])
# Save to CSV
os.makedirs('outputs', exist_ok=True)
synthetic_data.to_csv(output_path, index=False)
st.success(f"✅ Synthetic data saved to {output_path}")
def fetch_data(domain):
"""Fetches real data for the given domain and ensures it's a valid DataFrame."""
data_path = f"datasets/{domain}.csv"
if os.path.exists(data_path):
df = pd.read_csv(data_path)
if not isinstance(df, pd.DataFrame) or df.empty:
raise ValueError("❌ Loaded data is invalid!")
return df
else:
st.error(f"❌ Dataset for {domain} not found.")
return None
st.title("✨ AI-Powered Synthetic Dataset Generator")
st.write("Give a short description of the dataset you need, and AI will generate it for you using real data + GANs!")
# User input
user_prompt = st.text_input("Describe the dataset (e.g., 'Create dataset for hospital patients')", "")
domain = st.selectbox("Select Domain for Real Data", ["healthcare", "finance", "retail", "other"])
data = None
if st.button("Generate Schema"):
if user_prompt.strip():
with st.spinner("Generating schema..."):
schema = generate_schema(user_prompt)
if schema is None:
st.error("❌ Schema generation failed. Please check API response.")
else:
st.success("✅ Schema generated successfully!")
st.json(schema)
data = fetch_data(domain)
else:
st.warning("⚠️ Please enter a dataset description before generating the schema.")
if data is not None and schema is not None:
output_path = "outputs/synthetic_data.csv"
if st.button("Generate Synthetic Data"):
with st.spinner("Training GAN and generating synthetic data..."):
train_and_generate_synthetic(data, schema, output_path)
with open(output_path, "rb") as file:
st.download_button("Download Synthetic Data", file, file_name="synthetic_data.csv", mime="text/csv")
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