| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| import os | |
| from rag import local_retriever, global_retriever | |
| from transformers import LlamaTokenizer | |
| """ | |
| 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, | |
| search_strategy, | |
| top_p, | |
| ): | |
| if search_strategy == "Global": | |
| return global_retriever(message, 2, "multiple paragraphs") | |
| else: | |
| 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=6192, | |
| stream=True, | |
| temperature=1.0, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| return response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox( | |
| value="You are a medical assistant Chatbot. For any query that you don't know, you will say 'I don't know'. You will answer with the given information:", | |
| label="System message", | |
| ), | |
| gr.Dropdown( | |
| choices=["Local", "Global"], value="Local", label="Select search strategy" | |
| ), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |