import gradio import LlamaManager import os import huggingface_hub HF_API = huggingface_hub.HfApi() LLAMAMANAGER = LlamaManager.LlamaManager(os.environ.get("HF_KEY_2"), True) def store_generated_data(data): token = os.environ.get("HF_BOT") data = f"{data}" HF_API.comment_discussion("xqt/SyntheticMBPP2", 1, data, repo_type = "dataset", token = token) def authenticate(secret_textbox): global LLAMAMANAGER password_list = os.environ.get("PASSWORD_LIST") password_list = password_list.split(":") api_key = "" if secret_textbox in password_list: api_key = os.environ.get("HF_KEY") else: api_key = secret_textbox LLAMAMANAGER = LlamaManager.LlamaManager(api_key, True) def generate_categories(categories_count, seed, temperature, top_p, frequency_penalty): categories = LLAMAMANAGER.auto_generate_questions_categories( count = categories_count, seed = seed, temperature = temperature, top_p = top_p, frequency_penalty = frequency_penalty ) data = { "type": "generate_categories", "categories": categories, "count": categories_count, "seed": seed, "temperature": temperature, "top_p": top_p, "frequency_penalty": frequency_penalty } store_generated_data(data) return gradio.Dropdown(choices = categories, value = categories[0], label = "Select Category", interactive = True) def generate_shots(category, shots_count, seed, temperature, top_p, frequency_penalty): shots = LLAMAMANAGER.auto_generate_shots_for_category(category, shots_count, seed, temperature, top_p, frequency_penalty) shots = [[shot] for shot in shots] data = { "type": "generate_shots", "category": category, "shots": shots, "count": shots_count, "seed": seed, "temperature": temperature, "top_p": top_p, "frequency_penalty": frequency_penalty } store_generated_data(data) return gradio.DataFrame(value = shots, type = "array", label = "Generated Shots", interactive = False, headers = None) def generate_questions(questions_count, category, shots, seed, temperature, top_p, frequency_penalty): questions = LLAMAMANAGER.auto_generate_questions_from_shots(questions_count, category, shots, seed, temperature, top_p, frequency_penalty) questions = [[question] for question in questions] data = { "type": "generate_questions", "questions": questions, "count": questions_count, "category": category, "shots": shots, "seed": seed, "temperature": temperature, "top_p": top_p, "frequency_penalty": frequency_penalty } store_generated_data(data) return gradio.DataFrame(value = questions, type = "array", label = "Generated Shots", interactive = False, headers = None) with gradio.Blocks(fill_height=True) as base_app: gradio.Markdown("# Synthetic Python Programming Data Generation ⚙️") gradio.Markdown("# ❗️ Note: The data generated here by Llama3 and the settings used to generate it will be stored in the repository [here](https://huggingface.co/datasets/xqt/SyntheticMBPP2) for future use.") gradio.Markdown("# ❗️ Each successful interaction is saved [here](https://huggingface.co/datasets/xqt/SyntheticMBPP2/discussions/1)") gradio.Markdown("# ❗️ Feel free to use your own API key if the key here is rate limited. API Key is never stored in the repository.") gradio.Markdown("# ❗️ If you want to use a passcode, please text me.") gradio.Markdown("# Step 0: Use your own API Key/Passcode") with gradio.Row(): with gradio.Column(): __secret_textbox = gradio.Textbox(label = "API Key/Passcode", placeholder = "Enter your API Key/Passcode here", type = "password", interactive = True) with gradio.Column(): __passcode_authenticate = gradio.Button("Authenticate", scale = 2) gradio.Markdown("# Step 1: How many categories do you want to generate?") with gradio.Row(equal_height = True): with gradio.Column(scale = 2): __categories_count = gradio.Slider(minimum = 1, maximum = 20, step = 1, value = 10, label = "Number of Categories", interactive = True) with gradio.Column(): __categories_generate = gradio.Button("Generate Categories", scale = 2) with gradio.Accordion("Advanced Settings", open = False): with gradio.Row(): with gradio.Column(): __categories_temperature = gradio.Slider(minimum = 0.1, maximum = 2.0, step = 0.01, value = 1.0, label = "Temperature", interactive = True) __categories_top_p = gradio.Slider(minimum = 0.1, maximum = 0.99, step = 0.01, value = 0.9, label = "Top P", interactive = True) with gradio.Column(): __categories_frequency_penalty = gradio.Slider(minimum = -2.0, maximum = 2.0, step = 0.01, value = 0.0, label = "Frequency Penalty", interactive = True) __categories_seed = gradio.Slider(minimum = 0, maximum = 1000, step = 1, value = 123, label = "Seed", interactive = True) gradio.Markdown("# Step 2: Select a category to generate shots for and select the number of shots to generate") with gradio.Row(): with gradio.Column(scale = 2): __shots_category = gradio.Dropdown(choices = [], label = "Select Category", interactive = True) __shots_count = gradio.Slider(minimum = 2, maximum = 5, step = 1, value = 2, label = "Number of Shots", interactive = True) with gradio.Column(): __shots_generate = gradio.Button("Generate Shots", scale = 2) with gradio.Accordion("Advanced Settings", open = False): with gradio.Row(): with gradio.Column(): __shots_temperature = gradio.Slider(minimum = 0.1, maximum = 2.0, step = 0.01, value = 1.0, label = "Temperature", interactive = True) __shots_top_p = gradio.Slider(minimum = 0.1, maximum = 0.99, step = 0.01, value = 0.9, label = "Top P", interactive = True) with gradio.Column(): __shots_frequency_penalty = gradio.Slider(minimum = -2.0, maximum = 2.0, step = 0.01, value = 0.0, label = "Frequency Penalty", interactive = True) __shots_seed = gradio.Slider(minimum = 0, maximum = 1000, step = 1, value = 123, label = "Seed", interactive = True) __generated_shots = gradio.DataFrame(value = [], col_count = 1, type = "array", label = "Generated Shots", interactive = False, headers = None) gradio.Markdown("# Step 3: Generate Python Programming Questions for the generated shots") with gradio.Row(): with gradio.Column(scale = 2): __questions_count = gradio.Slider(minimum = 1, maximum = 30, step = 1, value = 10, label = "Number of Questions", interactive = True) with gradio.Column(): __questions_generate = gradio.Button("Generate Questions", scale = 2) with gradio.Accordion("Advanced Settings", open = False): with gradio.Row(): with gradio.Column(): __questions_temperature = gradio.Slider(minimum = 0.1, maximum = 2.0, step = 0.01, value = 1.0, label = "Temperature", interactive = True) __questions_top_p = gradio.Slider(minimum = 0.1, maximum = 0.99, step = 0.01, value = 0.9, label = "Top P", interactive = True) with gradio.Column(): __questions_frequency_penalty = gradio.Slider(minimum = -2.0, maximum = 2.0, step = 0.01, value = 0.0, label = "Frequency Penalty", interactive = True) __questions_seed = gradio.Slider(minimum = 0, maximum = 1000, step = 1, value = 123, label = "Seed", interactive = True) __generated_questions = gradio.DataFrame(value = [], col_count = 1, type = "array", label = "Generated Questions", interactive = False, headers = None) __passcode_authenticate.click(authenticate, inputs = [__secret_textbox], outputs = [] ) __categories_generate.click(generate_categories, inputs = [__categories_count, __categories_seed, __categories_temperature, __categories_top_p, __categories_frequency_penalty], outputs = [__shots_category] ) __shots_generate.click(generate_shots, inputs = [__shots_category, __shots_count, __shots_seed, __shots_temperature, __shots_top_p, __shots_frequency_penalty], outputs = [__generated_shots] ) __questions_generate.click(generate_questions, inputs = [__questions_count, __shots_category, __generated_shots, __questions_seed, __questions_temperature, __questions_top_p, __questions_frequency_penalty], outputs = [__generated_questions] ) if __name__ == "__main__": base_app.launch()