Create app.py

app.py

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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")