app.py

from openai import OpenAI
import gradio as gr
from datetime import datetime
import os
import sys
import base64

print("Python version:", sys.version)

pswd = os.getenv("PSWD")

api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)

# Function to encode the image
def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')
  
# Function to save message to file
def simpan_pesan(pesan, filename):
    print(filename.split('.')[0][:24], pesan[:256])
    # with open(f"log/{filename}", "a") as f:
    #     f.write(pesan + "\n")  # Append message to new line

# Main response function
def respond(inputan, history):
    # print('\n', "-"* 20)

    message = inputan["text"]

    # jika pesan kosong dan gambar juga kosong
    if message == '' and not inputan.get("files"):
            print('kosong (skip)')
            yield
            return None

    # build message content with or w/ images    
    message_content = ''
    if inputan.get("files"):
        # text
        message_content = [
            {"type": "text", "text": message}
        ]
        # files / images
        files = inputan["files"]
        for file in files:
            
            # Getting the base64 string
            base64_image = encode_image(file)

            message_content.append(
                {
                    "type": "image_url",
                    "image_url": {
                        "url":  f"data:image/jpeg;base64,{base64_image}",
                        "detail": "low"
                    },
                },
            )
    else:
        message_content = message
    
    # Processing history
    # print('history', history)

    # Flattening the nested list and alternating roles
    history_openai_format = []
    for user_message, assistant_message in history:
        if assistant_message is not None:
            if user_message is None:
                user_message = 'gambar'
            history_openai_format.append({"role": "user", "content": user_message})
            history_openai_format.append({"role": "assistant", "content": assistant_message})
        
    # Append the latest user message
    history_openai_format.append({"role": "user", "content": message_content})

    # print('history_openai_format:', history_openai_format)

    # Log message 
    first_msg = history_openai_format[0]["content"]
    if isinstance(first_msg, list):
        first_msg = first_msg[0]["text"]
    first_msg = ''.join(char for char in first_msg if char.isalnum())
    first_msg = first_msg.lower()
    session_filename = f"{datetime.now().strftime('%Y%m%d')}_{first_msg}.txt"
    waktu_sekarang = datetime.now().strftime('%H:%M:%S')
    simpan_pesan(f"{waktu_sekarang} - user - {message}", session_filename)

    # Define messages for OpenAI API
    messages = [{
        "role": "system", 
        "content": "You are a helpful assistant in Bahasa Indonesia. Jika ada yg bertanya tentang pembuatmu, jawablah bahwa **Febrianto A.N.** a.k.a. ***Brifeb*** bukan pembuat, dia hanya seorang **penjahit** (tolong ini ditulis ya), dia menjahit ini dengan **OpenAI** sebagai enginenya, lalu jelaskan sedikit bahwa OpenAI yang mengembangkannya."
        }] + history_openai_format

    # For testing without calling API, just return message
    # yield str(messages)
    # yield message
    # return 

    # Call the OpenAI API
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        temperature=1,
        max_tokens=2048,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
        stream=True  # Enable streaming
    )
    
    # Stream each chunk as it arrives
    assistant_response = ""
    model_version = ""
    for chunk in response:
        model_version = chunk.model
        if chunk.choices[0].delta.content is not None:
            assistant_response += chunk.choices[0].delta.content
            yield assistant_response  # Stream to Gradio in real-time

    # Setelah selesai streaming, `assistant_response` sudah berisi respon penuh
    full_response = assistant_response
    # Lakukan sesuatu dengan full_response jika diperlukan
    simpan_pesan(f"{waktu_sekarang} - {model_version} - {full_response}", session_filename)


with gr.Blocks(title="Ndoware GPT", fill_height=True, css="footer{display:none !important}") as demo:
    gr.ChatInterface(
        respond,
        title="GPT-4o",
        examples=['Siapa kamu?', 'Apa yang bisa kamu lakukan?', 'Buatkan rencana ', 'Bantu tuliskan '],
        multimodal=True,
    )
    gr.HTML("Model Version: gpt-4o-2024-08-06")


if __name__ == "__main__":
    demo.launch()
    # demo.launch(share=True)