Update synthetic_generator.py

synthetic_generator.py

@@ -1,69 +1,69 @@
-import pandas as pd
-from ctgan import CTGAN
-from sklearn.preprocessing import LabelEncoder
-import os
-import json
-import requests
-
-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)
-    print(f"✅ Synthetic data saved to {output_path}")
-
-def generate_schema(prompt):
-    """Fetches schema from an external API and validates JSON."""
-    API_URL = "https://api.example.com/schema"  # Replace with correct API URL
-    headers = {"Authorization": f"Bearer YOUR_HUGGINGFACE_TOKEN"}  # Add if needed
-
-    try:
-        response = requests.post(API_URL, json={"prompt": prompt}, headers=headers)
-        print("🔍 Raw API Response:", response.text)  # Debugging line
-
-        schema = response.json()
-        
-        # Validate required keys
-        if 'columns' not in schema or 'types' not in schema or 'size' not in schema:
-            raise ValueError("❌ Invalid schema format! Expected keys: 'columns', 'types', 'size'")
-
-        print("✅ Valid Schema Received:", schema)  # Debugging line
-        return schema
-
-    except json.JSONDecodeError:
-        print("❌ Failed to parse JSON response. API might be down or returning non-JSON data.")
-        return None
-    except requests.exceptions.RequestException as e:
-        print(f"❌ API request failed: {e}")
-        return None
-
-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:
-        raise FileNotFoundError(f"❌ Dataset for {domain} not found.")
+import pandas as pd
+from ctgan import CTGAN
+from sklearn.preprocessing import LabelEncoder
+import os
+import json
+import requests
+
+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)
+    print(f"✅ Synthetic data saved to {output_path}")
+
+def generate_schema(prompt):
+    """Fetches schema from an external API and validates JSON."""
+    API_URL = "https://infinitymatter-synthetic-data-generator-srijan.hf.space/run/predict"
+    headers = {"Authorization": f"Bearer hf_token"}  # Add if needed
+
+    try:
+        response = requests.post(API_URL, json={"prompt": prompt}, headers=headers)
+        print("🔍 Raw API Response:", response.text)  # Debugging line
+
+        schema = response.json()
+        
+        # Validate required keys
+        if 'columns' not in schema or 'types' not in schema or 'size' not in schema:
+            raise ValueError("❌ Invalid schema format! Expected keys: 'columns', 'types', 'size'")
+
+        print("✅ Valid Schema Received:", schema)  # Debugging line
+        return schema
+
+    except json.JSONDecodeError:
+        print("❌ Failed to parse JSON response. API might be down or returning non-JSON data.")
+        return None
+    except requests.exceptions.RequestException as e:
+        print(f"❌ API request failed: {e}")
+        return None
+
+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:
+        raise FileNotFoundError(f"❌ Dataset for {domain} not found.")