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import copy
import random
from time import sleep
import gradio as gr
from utils.model import Model
TEST = """ Test of Time. A Benchmark for Evaluating LLMs on Temporal Reasoning. Large language models (LLMs) have
showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal
reasoning tasks involving complex temporal logic. """
def generate_data_test():
"""Generator to yield words"""
temp = copy.deepcopy(TEST)
l1 = temp.split()
random.shuffle(l1)
temp = ' '.join(l1)
for word in temp.split(" "):
yield word + " "
def stream_data(content_list, model):
"""Stream data to three columns"""
outputs = ["" for _ in content_list]
# Use the gen method to handle batch generation
while True:
updated = False
for i, content in enumerate(content_list):
try:
word = next(model.gen([content], streaming=True)) # Wrap content in a list to match expected input type
outputs[i] += word
updated = True
except StopIteration:
pass
if not updated:
break
yield tuple(outputs)
def create_interface():
with gr.Blocks() as demo:
with gr.Group():
with gr.Row():
columns = [gr.Textbox(label=f"Column {i+1}", lines=10) for i in range(3)]
start_btn = gr.Button("Start Streaming")
def start_streaming():
content_list = [col.value for col in columns] # Get input texts from text boxes
for data in stream_data(content_list):
updates = [gr.update(value=data[i]) for i in range(len(columns))]
yield tuple(updates)
start_btn.click(
fn=start_streaming,
inputs=[],
outputs=columns,
show_progress=False
)
return demo
if __name__ == "__main__":
demo = create_interface()
demo.queue()
demo.launch() |