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import gradio as gr
import requests
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor, as_completed
from MonsterAPIClient import MClient
from MonsterAPIClient import MODELS_TO_DATAMODEL
client = MClient()
# Available models list
EXCLUSION_LIST = ['mpt-30B-instruct', 'llama2-7b-chat', 'openllama-13b-base']
available_models = list(set(list(MODELS_TO_DATAMODEL.keys())) - set(EXCLUSION_LIST))
def generate_model_output(model: str, input_text: str, temp: float = 0.98) -> str:
"""
Generate output from a specific model.
Parameters:
model (str): The name of the model.
input_text (str): The input prompt for the model.
temp (float, optional): The temperature value for text generation. Defaults to 0.98.
Returns:
str: The generated output text.
"""
try:
response = client.get_response(model, {
"prompt": input_text,
"temp": temp,
})
output = client.wait_and_get_result(response['process_id'])
return model, output
except Exception as e:
return model, f"Error occurred: {str(e)}"
def generate_output(selected_models: list, input_text: str, temp: float = 0.98,
available_models: list = available_models) -> list:
"""
Generate outputs from selected models using Monster API.
Parameters:
selected_models (list): List of selected model names.
input_text (str): The input prompt for the models.
temp (float, optional): The temperature value for text generation. Defaults to 0.98.
available_models (list, optional): List of available model names. Defaults to global variable.
Returns:
list: List of generated output texts corresponding to each model.
"""
outputs = {}
with ThreadPoolExecutor() as executor:
future_to_model = {executor.submit(generate_model_output, model, input_text, temp): model for model in selected_models}
for future in tqdm(as_completed(future_to_model), total=len(selected_models)):
model, output = future.result()
outputs[model] = output
ret_outputs = []
for model in available_models:
if model not in outputs:
ret_outputs.append("Model not selected!")
else:
ret_outputs.append(outputs[model].replace("\n", "<br>"))
return ret_outputs
output_components = [gr.outputs.Textbox(label=model) for model in available_models]
checkboxes = gr.inputs.CheckboxGroup(available_models, label="Select models to generate outputs:")
textbox = gr.inputs.Textbox(label="Input Prompt")
temp = gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.98, label="Temperature", step=0.01)
input_text = gr.Interface(
fn=generate_output,
inputs=[
checkboxes,
textbox,
temp
],
outputs=output_components,
live=False,
capture_session=True,
title="Monster API LLM Output Comparison.",
description="Generate outputs from selected models using Monster API.",
css="body {background-color: black}"
)
# Launch the Gradio app
input_text.launch()
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