import gradio as gr from gradio_client import Client import os import requests tulu = "https://tonic1-tulu.hf.space/--replicas/pqcgw/" 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 Tulu URL and returns the JSON response. """ response = requests.post(tulu, 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=""): client = Client(tulu) # Assuming Client is properly defined and tulu is a valid argument # Build the input prompt input_prompt = build_input_prompt(message, chatbot, system_prompt) # Ensure this function is defined try: # Adjust these parameters as needed max_new_tokens = 1200 temperature = 0.4 top_p = 0.9 repetition_penalty = 0.9 advanced = True # Making the prediction result = client.predict( input_prompt, # Using the built input prompt max_new_tokens, temperature, top_p, repetition_penalty, advanced, fn_index=0 ) # Extracting the response if result is not None and len(result) > 0: bot_message = result[0] # Assuming the response is in the first element return bot_message else: raise gr.Error("No response received from the model.") except Exception as e: error_msg = f"An error occurred: {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()