app.py

import gradio as gr
import os
from openai import OpenAI
import os.path
from datetime import datetime

################# Start PERSONA-SPECIFIC VALUES ######################
coach_code = os.getenv("COACH_CODE")
coach_name_short = os.getenv("COACH_NAME_SHORT")
coach_name_upper = os.getenv("COACH_NAME_UPPER")
sys_prompt_new = os.getenv("PROMPT_NEW")
theme=""
################# End PERSONA-SPECIFIC VALUES ######################

################# Start OpenAI-SPECIFIC VALUES ######################
# Initialize OpenAI API client with API key
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
# OpenAI model
openai_model = "gpt-3.5-turbo-0125"
################# End OpenAI-SPECIFIC VALUES ######################

tx = os.getenv("TX")
prefix = ""  #  "data/" if local or "/data/" if persistent in HF

############### CHAT ###################
def predict(user_input, history):
    max_length = 500
    transcript_file_path = f"{prefix}{coach_code}-transcript.txt"
    transcript = ""  # Initialize the transcript variable

    if user_input == tx:
        try:
            # Prepare the transcript for the Textbox output
            if os.path.exists(transcript_file_path):
                with open(transcript_file_path, "r", encoding="UTF-8") as file:
                    transcript = file.read()
                return transcript
        except FileNotFoundError:
            return "File 'transcript.txt' not found."
    elif len(user_input) > max_length:
        raise gr.Error(f"Input is TOO LONG. Max length is {max_length} characters. Try again.")

    history_openai_format = [
        {"role": "system", "content": "IDENTITY: " + sys_prompt_new}
    ]
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human})
        history_openai_format.append({"role": "assistant", "content": assistant})
    history_openai_format.append({"role": "user", "content": user_input})

    completion = client.chat.completions.create(
        model=openai_model,
        messages=history_openai_format,
        temperature=1.2,
        frequency_penalty=0.4,
        presence_penalty=0.1,
        stream=True
    )

    output_stream = ""
    try:
        for chunk in completion:
            if chunk.choices[0].delta.content is not None:
                output_stream = output_stream + (chunk.choices[0].delta.content)
        message_content = output_stream
    except StopAsyncIteration:
        pass

    # Append latest user and assistant messages to the transcript
    transcript += "Date/Time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n\n"
    transcript += f"YOU: {user_input}\n\n"
    transcript += f"{coach_name_upper}: {message_content}\n\n\n"
    # Write the updated transcript to the file
    with open(transcript_file_path, "a", encoding="UTF-8") as file:
        file.write(transcript)

    return message_content

#GUI
with gr.Blocks(theme) as demo:
    gr.ChatInterface(predict, submit_btn="Chat with "+ coach_name_short, retry_btn=None, undo_btn=None, clear_btn=None, autofocus=True)
demo.launch(show_api=False)