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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 | |
| import logging | |
| logging.basicConfig(level=logging.DEBUG) | |
| # """ | |
| # 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) | |
| logging.debug(f"Chat response: {response}") | |
| logging.debug(f"Chat response type: {type(response)}") | |
| yield 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() |