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import gradio as gr
from datasets import load_dataset
from typing import List,Dict
from gradio import ChatMessage
DATASETS_LIST = [("Self-GRIT/tuluv2-expanded-150k-part_v0-chat-format-syn-knowledge","part_v0")]
DATASETS = {f"{name}: {split}":load_dataset(name,split = split) for name,split in DATASETS_LIST}
LENS = {name:len(dataset) for name,dataset in DATASETS.items()}
KEY_MESSAGES_ORIGINAL = "messages_original"
KEY_MESSAGES_AUGMENTED = "messages_augmented"
def return_conversation_chat_message(history: List[Dict]):
conversation = []
for message in history:
conversation.append(ChatMessage(role = message["role"],content = message["content"]))
return conversation
def update_chatbot(value,dataset_name):
example = DATASETS[dataset_name][int(value)]
domain,_id = example["dataset"],example["id"]
oring_chat_history = return_conversation_chat_message(example[KEY_MESSAGES_ORIGINAL])
augmented_chat_history = return_conversation_chat_message(example[KEY_MESSAGES_AUGMENTED])
return oring_chat_history,augmented_chat_history,domain,_id
with gr.Blocks() as demo:
with gr.Group():
with gr.Row():
with gr.Column():
dropdown = gr.Dropdown(DATASETS.keys(), label="Select the dataset")
with gr.Column():
slider = gr.Slider(minimum=0, maximum=48127, step=1, label="Select the example")
with gr.Group():
with gr.Row():
with gr.Column():
domain_text = gr.Textbox("Domain")
with gr.Column():
id_text = gr.Textbox("ID")
with gr.Row():
gr.Markdown("Original Chat")
with gr.Row():
chatbot_original = gr.Chatbot(type = "messages")
with gr.Group():
with gr.Row():
gr.Markdown("Augmented Chat")
with gr.Row():
chatbot_augmented = gr.Chatbot(type = "messages")
slider.change(fn=update_chatbot, inputs=[slider,dropdown], outputs=[chatbot_original,chatbot_augmented,domain_text,id_text])
demo.launch() |