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| SYSTEM_PROMPT = "Your job as an LLM is to accurately convert complex recipes from the internet into easy-to-understand formats. You should be able to provide helpful tips and tricks, as well as answer any cooking-related questions. Be precise and clear, but also make sure to maintain the integrity of the original recipe." | |
| TITLE = "Recipe Assistant" | |
| EXAMPLE_INPUT = "Can you provide me with a step-by-step guide for making chicken parmesan?" | |
| 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() |