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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
# """
# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
# """
# client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# messages = [{"role": "system", "content": system_message}]
# for val in history:
# if val[0]:
# messages.append({"role": "user", "content": val[0]})
# if val[1]:
# messages.append({"role": "assistant", "content": val[1]})
# messages.append({"role": "user", "content": message})
# response = ""
# for message in client.chat_completion(messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p):
# token = message.choices[0].delta.content
# response += token
# yield response
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)
})
cfg.description = f'''
<h4 style="text-align: center;">{cfg.description}</h4>
'''
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)
return response
def random_fun(message, history):
return message + '!'
demo = gr.ChatInterface(respond, title = cfg.title, description = cfg.description)
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
# demo = gr.ChatInterface(respond)
if __name__ == "__main__":
demo.launch() |