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import gradio as gr
from transformers import pipeline
import argparse
from run_llm import main
# Initialize the GPT-2 pipeline
pipe = pipeline("text-generation", model="gpt2")
theme = gr.themes.Soft()
# Function that generates text based on instruction-based prompting
#def generate_text(input_instruction):
# # Use the input instruction to generate text
# generated_text = pipe(input_instruction, max_length=500)[0]['generated_text']
# return generated_text
# Create a function that takes 3 inputs:
# - A prompt which will be a random string
# - From the first dropdown select the task (1,2,3)
# - From the second dropdown select the model type
# use run_llm.py to feed the models and then output 3 results in 3 output boxes, one for each strategy (strategy 1, 2 and 3)
def generate_text(prompt, task_number, model_type):
generated_text = pipe(prompt, max_length=500)[0]['generated_text']
return generated_text
# Define example instructions for testing
instruction_examples = [
["Describe the origin of the universe"],
["Explain the concept of artificial intelligence"],
["Describe the most common types of cancer"],
]
# Function that echoes the input text
#def echo_text(input_text):
# return input_text
with gr.Interface(
fn=generate_text,
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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