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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 Shots", 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() |