Old generation code

app.py

@@ -7,43 +7,6 @@ import gradio as gr
 from synthgenai import DatasetConfig, DatasetGeneratorConfig, LLMConfig, InstructionDatasetGenerator, PreferenceDatasetGenerator,RawDatasetGenerator,SentimentAnalysisDatasetGenerator, SummarizationDatasetGenerator, TextClassificationDatasetGenerator
 
 
-def validate_inputs(*args):
-    """
-    Validate that all required inputs are filled.
-
-    Args:
-        *args: The input values to validate.
-
-    Returns:
-        bool: True if all required inputs are filled, False otherwise.
-    """
-    for arg in args:
-        if not arg:
-            return False
-    return True
-
-stop_event = asyncio.Event()
-
-def stop_generation():
-    """
-    Stop the dataset generation process.
-    """
-    stop_event.set()
-
-def get_hf_token(hf_token):
-    """
-    Retrieve the Hugging Face token from the provided input.
-
-    Args:
-        hf_token (str): The Hugging Face token.
-
-    Returns:
-        str: The Hugging Face token.
-    """
-    if not hf_token:
-        raise ValueError("Hugging Face token not provided.")
-    return hf_token
-
 def generate_synthetic_dataset(
     llm_model,
     temperature,
@@ -80,32 +43,11 @@ def generate_synthetic_dataset(
     Returns:
         str: A message indicating the result of the dataset generation.
     """
-
-    hf_token = get_hf_token(hf_token)
     os.environ["HF_TOKEN"] = hf_token
 
     for var in llm_env_vars.split(","):
-        if "=" in var:
-            key, value = var.split("=", 1)
-            os.environ[key.strip()] = value.strip()
-
-    # Validate inputs
-    if not validate_inputs(
-        llm_model,
-        temperature,
-        top_p,
-        max_tokens,
-        dataset_type,
-        topic,
-        domains,
-        language,
-        num_entries,
-        hf_token,
-        hf_repo_name,
-        llm_env_vars,
-    ):
-        return "All fields except API Base and API Key must be filled."
-
+        key, value = var.split("=")
+        os.environ[key.strip()] = value.strip()
 
     llm_config = LLMConfig(
         model=llm_model,
@@ -142,11 +84,8 @@ def generate_synthetic_dataset(
     else:
         return "Invalid dataset type"
 
-    
     dataset = asyncio.run(generator.agenerate_dataset())
     dataset.save_dataset(hf_repo_name=hf_repo_name)
-        
-
     return "Dataset generated and saved successfully."
 
 def ui_main():