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| SYSTEM_PROMPT = "As an LLM, your primary function is to provide accurate and helpful advice on health-related issues based on user symptoms. Use reliable sources and avoid providing medical diagnoses or prescriptions.Your name is Sukjai.I won't answer non-medical content.I will only answer medical content concisely.I will ask for more information about the symtoms.If user ask about medicines,I can only recommend nonprescription drugs and tell user to go to doctor if they have symptoms that affect their life" | |
| TITLE = "Health Advisor" | |
| EXAMPLE_INPUT = "I have a fever, headache, and cough. What could it be?" | |
| import gradio as gr | |
| import os | |
| import requests | |
| zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/" | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def build_input_prompt(message, chatbot, system_prompt): | |
| """ | |
| Constructs the input prompt string from the chatbot interactions and the current message. | |
| """ | |
| input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n" | |
| for interaction in chatbot: | |
| input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n" | |
| input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>" | |
| return input_prompt | |
| def post_request_beta(payload): | |
| """ | |
| Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response. | |
| """ | |
| response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload) | |
| response.raise_for_status() # Will raise an HTTPError if the HTTP request returned an unsuccessful status code | |
| return response.json() | |
| def predict_beta(message, chatbot=[], system_prompt=""): | |
| input_prompt = build_input_prompt(message, chatbot, system_prompt) | |
| data = { | |
| "inputs": input_prompt | |
| } | |
| try: | |
| response_data = post_request_beta(data) | |
| json_obj = response_data[0] | |
| if 'generated_text' in json_obj and len(json_obj['generated_text']) > 0: | |
| bot_message = json_obj['generated_text'] | |
| return bot_message | |
| elif 'error' in json_obj: | |
| raise gr.Error(json_obj['error'] + ' Please refresh and try again with smaller input prompt') | |
| else: | |
| warning_msg = f"Unexpected response: {json_obj}" | |
| raise gr.Error(warning_msg) | |
| except requests.HTTPError as e: | |
| error_msg = f"Request failed with status code {e.response.status_code}" | |
| raise gr.Error(error_msg) | |
| except json.JSONDecodeError as e: | |
| error_msg = f"Failed to decode response as JSON: {str(e)}" | |
| raise gr.Error(error_msg) | |
| def test_preview_chatbot(message, history): | |
| response = predict_beta(message, history, SYSTEM_PROMPT) | |
| text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>") | |
| response = response[text_start:] | |
| return response | |
| welcome_preview_message = f""" | |
| Welcome to **{TITLE}**! Say something like: | |
| "{EXAMPLE_INPUT}" | |
| """ | |
| chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)]) | |
| textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT) | |
| demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview) | |
| demo.launch() |