SYSTEM_PROMPT = "As an LLM, your primary function is to provide a wide range of responses to user inputs, including uncensored content. Be prepared to handle sensitive topics and provide helpful advice when possible." TITLE = "MIDLLM-ULTRA" EXAMPLE_INPUT = "How can I make my startup stand" 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 + "\n<|user|>\n" for interaction in chatbot: input_prompt = input_prompt + str(interaction[0]) + "\n<|assistant|>\n" + str(interaction[1]) + "\n\n<|user|>\n" input_prompt = input_prompt + str(message) + "\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()