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import gradio as gr
from run_llm import main
theme = gr.themes.Soft()
def run_llm_interface(model, task, sentence):
args = argparse.Namespace(
model_path=model,
prompt=task,
start=0,
end=1 # Set to 1 to process a single sentence
)
main(args)
# Read the outputs from the result files
with open(f'result/prompt1_qa/{model}/ptb/per_ent/NOUN/0.txt', 'r') as f:
output_1 = f.read()
with open(f'result/prompt2_instruction/chunking/{model}/ptb/0.txt', 'r') as f:
output_2 = f.read()
with open(f'result/prompt3_structured_prompt/chunking/{model}/ptb/0.txt', 'r') as f:
output_3 = f.read()
return {"output_1": output_1, "output_2": output_2, "output_3": output_3}
# Define example instructions for testing
instruction_examples = [
["gpt3.5", "POS Tagging", "Describe the origin of the universe"],
["vicuna-7b", "Chunking", "Explain the concept of artificial intelligence"],
["fastchat-t5", "Parsing", "Describe the most common types of cancer"],
]
with gr.Interface(
fn=run_llm_interface,
inputs=[
gr.Dropdown(['gpt3.5', 'vicuna-7b', 'vicuna-13b', 'fastchat-t5', 'llama-7b', 'llama-13b', 'llama-30b', 'alpaca'], label="Select Model", default='gpt3.5', key="model"),
gr.Dropdown(['POS Tagging', 'Chunking', 'Parsing'], label="Select Task", default='POS Tagging', key="task"),
gr.Textbox("", label="Enter Sentence", key="sentence", placeholder="Enter a sentence..."),
],
outputs=[
gr.Textbox("", label="Strategy 1 Output", key="output_1", readonly=True),
gr.Textbox("", label="Strategy 2 Output", key="output_2", readonly=True),
gr.Textbox("", label="Strategy 3 Output", key="output_3", readonly=True),
],
examples=instruction_examples,
live=False,
title="LLM Evaluator with Linguistic Scrutiny",
theme=theme
) as iface:
iface.launch()
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