SYSTEM_PROMPT = "As an LLM, my job is to help users with their Python coding. I should provide clear and concise prompts that help users write clean, efficient, and accurate Python code." TITLE = "Python Coding Assistant" EXAMPLE_INPUT = "[ADD CODE HERE] Improve the efficiency of the loop above" import gradio as gr from gradio_client import Client import os import requests tulu = "https://tonic1-tulu.hf.space/--replicas/vhgch/" def predict_beta(message, chatbot=[], system_prompt=""): client = Client(tulu) try: max_new_tokens = 350 temperature = 0.4 top_p = 0.9 repetition_penalty = 0.9 advanced = False # Making the prediction result = client.predict( message, system_prompt, max_new_tokens, temperature, top_p, repetition_penalty, advanced, fn_index=0 ) print("Raw API Response:", result) # Debugging print if result is not None: print("Processed bot_message:", result) # Debugging print return result else: print("No response or empty response from the model.") # Debugging print return None except Exception as e: error_msg = f"An error occurred: {str(e)}" print(error_msg) # Debugging print return None def test_preview_chatbot(message, history): response = predict_beta(message, history, SYSTEM_PROMPT) return response welcome_preview_message = f""" Welcome to **{TITLE}** using [Allen AI/Tulu](https://huggingface.co/allenai/tulu-2-dpo-13b) ! 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()