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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() |