App

app.py

@@ -0,0 +1,253 @@
+"""The UI file for the SynthGenAI package."""
+
+import os
+import asyncio
+
+import gradio as gr
+
+from .data_model import DatasetConfig, DatasetGeneratorConfig, LLMConfig
+from .dataset_generator import (
+    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
+
+
+def generate_synthetic_dataset(
+    llm_model,
+    temperature,
+    top_p,
+    max_tokens,
+    api_base,
+    api_key,
+    dataset_type,
+    topic,
+    domains,
+    language,
+    additional_description,
+    num_entries,
+    hf_token,
+    hf_repo_name,
+    llm_env_vars,
+):
+    """
+    Generate a dataset based on the provided parameters.
+
+    Args:
+        llm_model (str): The LLM model to use.
+        temperature (float): The temperature for the LLM.
+        top_p (float): The top_p value for the LLM.
+        max_tokens (int): The maximum number of tokens for the LLM.
+        api_base (str): The API base URL.
+        api_key (str): The API key.
+        dataset_type (str): The type of dataset to generate.
+        topic (str): The topic of the dataset.
+        domains (str): The domains for the dataset.
+        language (str): The language of the dataset.
+        additional_description (str): Additional description for the dataset.
+        num_entries (int): The number of entries in the dataset.
+        hf_token (str): The Hugging Face token.
+        hf_repo_name (str): The Hugging Face repository name.
+        llm_env_vars (str): Comma-separated environment variables for the LLM.
+
+    Returns:
+        str: A message indicating the result of the dataset generation.
+    """
+    os.environ["HF_TOKEN"] = hf_token
+
+    for var in llm_env_vars.split(","):
+        key, value = var.split("=")
+        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."
+
+    if api_base and api_key:
+        llm_config = LLMConfig(
+            model=llm_model,
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=max_tokens,
+            api_base=api_base,
+            api_key=api_key,
+        )
+    else:
+        llm_config = LLMConfig(
+            model=llm_model,
+            temperature=temperature,
+            top_p=top_p,
+            max_tokens=max_tokens,
+        )
+
+    dataset_config = DatasetConfig(
+        topic=topic,
+        domains=domains.split(","),
+        language=language,
+        additional_description=additional_description,
+        num_entries=num_entries,
+    )
+
+    dataset_generator_config = DatasetGeneratorConfig(
+        llm_config=llm_config,
+        dataset_config=dataset_config,
+    )
+
+    if dataset_type == "Raw":
+        generator = RawDatasetGenerator(dataset_generator_config)
+    elif dataset_type == "Instruction":
+        generator = InstructionDatasetGenerator(dataset_generator_config)
+    elif dataset_type == "Preference":
+        generator = PreferenceDatasetGenerator(dataset_generator_config)
+    elif dataset_type == "Sentiment Analysis":
+        generator = SentimentAnalysisDatasetGenerator(dataset_generator_config)
+    elif dataset_type == "Summarization":
+        generator = SummarizationDatasetGenerator(dataset_generator_config)
+    elif dataset_type == "Text Classification":
+        generator = TextClassificationDatasetGenerator(dataset_generator_config)
+    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():
+    """
+    Launch the Gradio UI for the SynthGenAI dataset generator.
+    """
+    with gr.Blocks(
+        title="SynthGenAI Dataset Generator",
+        css="footer {visibility: hidden}",
+        theme="ParityError/Interstellar",
+    ) as demo:
+        gr.Markdown(
+            """
+            <div style="text-align: center;">
+                <img src="https://raw.githubusercontent.com/Shekswess/synthgenai/refs/heads/main/docs/assets/logo_header.png" alt="Header Image" style="display: block; margin-left: auto; margin-right: auto; width: 50%;"/>
+                <h1>SynthGenAI Dataset Generator</h1>
+            </div>
+            """
+        )
+
+        with gr.Row():
+            llm_model = gr.Textbox(
+                label="LLM Model", placeholder="model_provider/model_name"
+            )
+            temperature = gr.Slider(
+                label="Temperature", minimum=0.0, maximum=1.0, step=0.1, value=0.5
+            )
+            top_p = gr.Slider(
+                label="Top P", minimum=0.0, maximum=1.0, step=0.1, value=0.9
+            )
+            max_tokens = gr.Number(label="Max Tokens", value=2048)
+            api_base = gr.Textbox(label="API Base", placeholder="API Base - Optional")
+            api_key = gr.Textbox(
+                label="API Key", placeholder="Your API Key - Optional", type="password"
+            )
+
+        with gr.Row():
+            dataset_type = gr.Dropdown(
+                label="Dataset Type",
+                choices=[
+                    "Raw",
+                    "Instruction",
+                    "Preference",
+                    "Sentiment Analysis",
+                    "Summarization",
+                    "Text Classification",
+                ],
+            )
+            topic = gr.Textbox(label="Topic", placeholder="Dataset topic")
+            domains = gr.Textbox(label="Domains", placeholder="Comma-separated domains")
+            language = gr.Textbox(
+                label="Language", placeholder="Language", value="English"
+            )
+            additional_description = gr.Textbox(
+                label="Additional Description",
+                placeholder="Additional description",
+                value="",
+            )
+            num_entries = gr.Number(label="Number of Entries", value=1000)
+
+        with gr.Row():
+            hf_token = gr.Textbox(
+                label="Hugging Face Token",
+                placeholder="Your HF Token",
+                type="password",
+                value=None,
+            )
+            hf_repo_name = gr.Textbox(
+                label="Hugging Face Repo Name",
+                placeholder="organization_or_user_name/dataset_name",
+                value=None,
+            )
+            llm_env_vars = gr.Textbox(
+                label="LLM Environment Variables",
+                placeholder="Comma-separated environment variables (e.g., KEY1=VALUE1, KEY2=VALUE2)",
+                value=None,
+            )
+
+        generate_button = gr.Button("Generate Dataset")
+        output = gr.Textbox(label="Operation Result", value="")
+
+        generate_button.click(
+            generate_synthetic_dataset,
+            inputs=[
+                llm_model,
+                temperature,
+                top_p,
+                max_tokens,
+                api_base,
+                api_key,
+                dataset_type,
+                topic,
+                domains,
+                language,
+                additional_description,
+                num_entries,
+                hf_token,
+                hf_repo_name,
+                llm_env_vars,
+            ],
+            outputs=output,
+        )
+
+    demo.launch(inbrowser=True, favicon_path=None)
+
+
+if __name__ == "__main__":
+    ui_main()

requirements.txt

@@ -0,0 +1 @@
+synthgenai