from omegaconf import OmegaConf
from query import VectaraQuery
import os
import gradio as gr
def isTrue(x) -> bool:
if isinstance(x, bool):
return x
return x.strip().lower() == 'true'
corpus_ids = str(os.environ['corpus_ids']).split(',')
cfg = OmegaConf.create({
'customer_id': str(os.environ['customer_id']),
'corpus_ids': corpus_ids,
'api_key': str(os.environ['api_key']),
'title': os.environ['title'],
'description': os.environ['description'],
'source_data_desc': os.environ['source_data_desc'],
'streaming': isTrue(os.environ.get('streaming', False)),
'prompt_name': os.environ.get('prompt_name', None)
})
vq = VectaraQuery(cfg.api_key, cfg.customer_id, cfg.corpus_ids, cfg.prompt_name)
def respond(message, history):
if cfg.streaming:
# Call stream response and stream output
stream = vq.submit_query_streaming(message)
outputs = ""
for output in stream:
outputs += output
yield outputs
else:
# Call non-stream response and return message output
response = vq.submit_query(message)
yield response
cfg.title = f'''{cfg.title}
'''
cfg.description = f''' {cfg.description}
This demo uses Retrieval Augmented Generation to ask questions about {cfg.source_data_desc}'''
image_html = '''
'''
with gr.Blocks() as demo:
gr.HTML()
gr.ChatInterface(respond, title = cfg.title, description = cfg.description,
chatbot = gr.Chatbot(value = [[None, "How may I help you?"]], scale=3), css = custom_css)
# header = f'''
#
#
# {cfg.title}
# {cfg.description}
#
#
# '''
# demo = gr.ChatInterface(respond, chatbot = gr.Chatbot(value = [[None, "How may I help you?"]]), head = header)
if __name__ == "__main__":
demo.launch()