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from omegaconf import OmegaConf
from query import VectaraQuery
import os
from PIL import Image
import gradio as gr
from huggingface_hub import InferenceClient
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'''<h1 style="text-align: center;">{cfg.title}</h1>'''
# cfg.description = f'''<h2 style="text-align: center;">{cfg.description}</h2>'''
# demo = gr.ChatInterface(respond, title = cfg.title, description = cfg.description, chatbot = gr.Chatbot(value = [[None, "How may I help you?"]]))
cfg.title = f'''<h1 style="text-align: center;">{cfg.title}</h1>'''
cfg.description = f'''<h2 style="text-align: center;">{cfg.description}</h2>'''
header = f'''
<h1 style="text-align: center;">{cfg.title}</h1>
<h2 style="text-align: center;">{cfg.description}</h2>
'''
header = "Hello Test"
demo = gr.ChatInterface(respond, chatbot = gr.Chatbot(value = [[None, "How may I help you?"]]), head = header)
if __name__ == "__main__":
demo.launch() |