chatbot-GPT/main.py

import datetime
import logging
import os
import json
import re
import time
import uuid
import random

import openai
import requests
from openai import OpenAI
from flask import Flask, request, jsonify, send_from_directory, url_for

from convert import alpaca_to_chatgpt, csv_to_jsonl

app = Flask(__name__)
ssl = None
# ssl =('/etc/ssl/sample.crt', '/etc/ssl/sample.pem')

app.openai_key = os.environ.get("OPENAI_KEY", "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")
app.openai_client = OpenAI(api_key=app.openai_key)

#logging.basicConfig(level=logging.DEBUG, filename='/jkt-disk-01/app/mms/chatgpt-apache/chatgpt.log', format='%(asctime)s %(message)s')

app.chat_messages = [
    {"role": "system",
     "content": "Please respond professionally and in a friendly manner, using the same language as the original request. Use emoji responsibly."}
]
app.translate_messages = [
    {"role": "system",
     "content": "Please translate using the requested language."}
]
app.suggest_messages = [
    {"role": "system",
     "content": "Please suggest reply messages based on the previous conversations and the user's request."}
]
app.recommend_messages = [
    {"role": "system",
     "content": "Give normalized total weight of each category in json based on headlines"
     }
]
app.summary_messages = [
    {"role": "system",
     "content": "Please summarize an article."
     }
]
UPLOAD_FOLDER = 'files'
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER


@app.route('/files/<name>')
def download_file(name):
    return send_from_directory(app.config["UPLOAD_FOLDER"], name)


@app.route('/', methods=['GET', 'POST'])
def test():
    return jsonify({"status": "0"})

def roulette() -> str:
    roulette_arr = [(80, "gpt-4o-mini"), (20, "gpt-4o")]
    rand_num = random.randrange(0, 99)
    model_name = ""
    n = 0
    j = 0
    while rand_num >= n:
        n += roulette_arr[j][0]
        model_name = roulette_arr[j][1]
        print(model_name)
        j += 1
    return model_name

def prune_message(message: dict):
    m = message.copy()
    for k in list(m.keys()):
        if k != 'role' and k != 'content':
            m.pop(k)
    return m


def recommend(headlines, category):
    chat_messages = app.recommend_messages.copy()
    try:
        json_payload = {
            "role": "user",
            "content": f"""{headlines}
                                    Berikan nilai berat masing-masing kategori, jumlahkan dan normalisasikan:
                                    {category}
                                    Berikan dalam bentuk json
                                    """
        }
        chat_messages.append(json_payload)
        time.sleep(3)
        json_response = app.openai_client.chat.completions.create(model="gpt-4o-mini",
                                                                  messages=chat_messages,
                                                                  response_format={"type": "json_object"}
                                                                  )
        result = {"status": "ok", "message": json.loads(json_response.choices[0].message.content)}
    except openai.APITimeoutError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": e.message}, 408
    except openai.NotFoundError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": json.loads(e.response.content)['error']['message']}, e.status_code
    except Exception as error_print:
        app.logger.exception("error")
        result = {"status": "error", "message": "Please try again"}, 405
    return result


def vision(message, image_url=None, image_b64=None):
    chat_messages = app.chat_messages.copy()
    url = ""
    if image_url:
        url = f"{image_url}"
    elif image_b64:
        url = f"data:image/jpeg;base64,{image_b64}"
    try:
        json_payload = {
            "role": "user",
            "content": [
                {"type": "text", "text": message},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": url,
                    },
                },
            ],
        }
        chat_messages.append(json_payload)
        time.sleep(3)
        json_response = app.openai_client.chat.completions.create(
            model="gpt-4o",
            messages=chat_messages,
            max_tokens=500
        )
        result = {"role": "assistant", "content": json_response.choices[0].message.content}
    except openai.APITimeoutError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": e.message}, 408
    except openai.NotFoundError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": json.loads(e.response.content)['error']['message']}, e.status_code
    except Exception as e:
        app.logger.exception("error")
        result = {"status": "error", "message": "Please try again"}, 405
    return result

def suggest_schedule(sched, message: list, assistant_id=None, timestamp=None):
    result = {}
    if timestamp is None:
        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
    message[-1]["content"] = f"{message[-1]['content']} Sekarang jam {timestamp}.\n\n{json.dumps(sched)}"
    try:
        if assistant_id:
            runs = app.openai_client.beta.threads.create_and_run_poll(
                assistant_id=assistant_id,
                thread={
                    "messages": message
                }
            )
            if runs.status == "completed":
                messages = list(app.openai_client.beta.threads.messages.list(thread_id=runs.thread_id, run_id=runs.id))
                message_content = messages[0].content[0].text
                app.logger.info(message_content.value)
                pattern = re.compile(r"【\d+:\d+†\(?source\)?】")
                filtered_message = pattern.sub("", message_content.value)
                result = {"role": "assistant", "content": filtered_message}
        else:
            json_response = app.openai_client.chat.completions.create(model="gpt-4o",
                                                                      messages=message,
                                                                      temperature=0.7)
            response_message = json_response.choices[0].message.content
            result = {"role": "assistant", "content": response_message}
    except openai.APITimeoutError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": e.message}, 408
    except openai.NotFoundError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": json.loads(e.response.content)['error']['message']}, e.status_code
    except Exception as e:
        app.logger.exception("error")
        result = {"status": "error", "message": "Please try again"}, 405
    return result


def convert_to_schedule(message: list):
    result = {}
    message[-1]["content"] = f'{message[-1]["content"]} Ubah menjadi format json seperti berikut:\n\n{{"start_time": "2025-10-01 11:00", "end_time": "2025-10-01 13:00", "description": "Deskripsi kegiatan" }}'
    try:
        json_response = app.openai_client.chat.completions.create(
            model="gpt-4o",
            messages=message,
            temperature=0.2,
            response_format={"type": "json_object"}
        )
        response_message = json_response.choices[0].message.content
        result = json.loads(response_message)
    except openai.APITimeoutError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": e.message}, 408
    except openai.NotFoundError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": json.loads(e.response.content)['error']['message']}, e.status_code
    except Exception as e:
        app.logger.exception("error")
        result = {"status": "error", "message": "Please try again"}, 405
    return result

@app.route('/gpt', methods=['POST'])
def gpt():
    assistant_id = ""
    assistant = None
    chat_messages = app.chat_messages.copy()
    chat_model = "gpt-4o-mini"
    use_video = False
    suggest = False
    summarize = False
    expression = False
    predict_q = 0
    max_char_msg = 500
    max_resp_token = 600
    category = []
    headlines = []
    image_url = ""
    num_choices = 1
    json_payload = request.get_json()
    if not json_payload:
        json_payload = []
    has_named_params = False
    app.logger.info("Request: " + str(json_payload))
    if isinstance(json_payload, dict):
        has_named_params = 'payload' in json_payload
        if 'payload' in json_payload:
            if 'predict_q' in json_payload:
                predict_q = 5 if json_payload['predict_q'] > 4 else 0 if json_payload['predict_q'] < 1 else \
                    json_payload['predict_q']
            if 'num_choices' in json_payload:
                num_choices = 5 if json_payload['num_choices'] > 4 else 1 if json_payload['num_choices'] < 2 else \
                    json_payload['num_choices']
            if 'use_video' in json_payload:
                use_video = json_payload['use_video'] == "1"
            if 'chat_model' in json_payload and 'assistant_id' not in json_payload:
                chat_model = json_payload['chat_model']
                max_resp_token = 2048
            if 'expression' in json_payload:
                expression = json_payload['expression'] == "1"
            if 'translate' in json_payload:
                chat_messages = app.translate_messages.copy()
                json_payload['payload'][-1]['content'] = json_payload['payload'][-1][
                                                             'content'] + f" (Translate to {json_payload['translate']})"
            elif 'suggest' in json_payload:
                suggest = json_payload['suggest'] == "1"
                if suggest:
                    chat_messages = app.suggest_messages.copy()
                else:
                    chat_messages = app.chat_messages.copy()
                json_payload['payload'][-1]['content'] = json_payload['payload'][-1][
                                                             'content'] + f" What can I say to him/her?"
            elif 'summarize' in json_payload:
                summarize = json_payload['summarize'] == "1"
                if summarize:
                    chat_messages = app.summary_messages.copy()
                    max_char_msg = 2000
                    max_resp_token = 1000
                else:
                    chat_messages = app.chat_messages.copy()
                json_payload['payload'][-1]['content'] = f"Please summarize this article:\n" + \
                                                         json_payload['payload'][-1]['content']
            elif 'assistant_id' in json_payload:
                assistant_id = json_payload['assistant_id']
                assistant = app.openai_client.beta.assistants.retrieve(assistant_id=assistant_id)
                chat_model = assistant.model
            else:
                chat_messages = app.chat_messages.copy()
            if 'schedule' in json_payload:
                timestamp = None
                if 'timestamp' in json_payload:
                    timestamp = json_payload["timestamp"]
                sched = json_payload["schedule"].copy()
                message = json_payload["payload"].copy()
                return suggest_schedule(sched, message, assistant_id, timestamp)
            elif 'convert_schedule' in json_payload:
                if json_payload['convert_schedule'] == "1":
                    return convert_to_schedule(json_payload['payload'].copy())
            json_payload = json_payload['payload']
            if isinstance(json_payload, dict):
                json_payload = [json_payload]
        elif 'greeting' in json_payload:
            chat_messages = app.chat_messages.copy()
            company_name = json_payload['greeting']['company_name']
            timestamp = json_payload['greeting']['timestamp']
            islamic_message = f"Apakah Nama '{company_name}' terdapat unsur islami? Jawab dengan 'Ya' atau 'Tidak'"
            islam_messages = app.chat_messages.copy()
            islam_messages.append({
                "role": "user",
                "content": islamic_message
            })
            islamic_response = app.openai_client.chat.completions.create(model="gpt-4o-mini",
                                                                         messages=islam_messages,
                                                                         max_tokens=2, temperature=0.5)
            if 'Ya' in islamic_response.choices[0].message.content:
                greeting_message = f"Buatkan respons chatbot berupa greeting dari chat perusahaan bernama {company_name} pada jam {timestamp}, tidak perlu mention waktu, dan jawab dengan 'Assalamu'alaikum...' terlebih dahulu"
            else:
                greeting_message = f"Buatkan respons chatbot berupa greeting dari chat perusahaan bernama {company_name} pada jam {timestamp}, tidak perlu mention waktu"
            json_payload = [
                {
                    "role": "user",
                    "content": greeting_message
                }
            ]
        elif 'recommend' in json_payload:
            headlines = json_payload['recommend']['headlines']
            category = json_payload['recommend']['category']
            return recommend(headlines, category)
        elif 'image_url' in json_payload:
            image = json_payload['image_url']
            message = json_payload["message"] if 'message' in json_payload else "Ini gambar apa?"
            return vision(message, image_url=image)
        elif 'image_b64' in json_payload:
            image = json_payload['image_b64']
            message = json_payload["message"] if 'message' in json_payload else "Ini gambar apa?"
            return vision(message, image_b64=image_url)
        else:
            app.logger.info("This request use old json format")
            chat_messages = app.chat_messages.copy()
    app.logger.info("Chat Messages:")
    app.logger.info(str(chat_messages))
    json_payload = json_payload[-5:]
    if assistant_id:
        chat_messages = []
    for message in json_payload:
        p_message = prune_message(message)
        if p_message['role'] == 'user':
            content = p_message['content'].lower()
        else:
            content = p_message['content']
        content_arr = content.split(" ")
        new_content_arr = content[:max_char_msg].split(" ")
        new_content_len = len(new_content_arr)
        arr = []
        for i in range(new_content_len):
            arr.append(content_arr[i])
        p_message['content'] = " ".join(arr)
        chat_messages.append(p_message)
    app.logger.info(chat_messages)
    result = {}
    try:
        n = num_choices
        if "gpt-3.5-turbo" or "gpt-4o-mini" in chat_model:
            chat_model = roulette()
        app.logger.info(f"Model used: {chat_model}")
        if assistant_id and not suggest:
            runs = app.openai_client.beta.threads.create_and_run_poll(
                assistant_id=assistant_id,
                thread={
                    "messages": chat_messages
                }
            )
            if runs.status != "completed":
                result = {"role": "assistant", "content": "Maaf, saat ini saya sedang sibuk. Coba beberapa saat lagi."}
            else:
                messages = list(app.openai_client.beta.threads.messages.list(thread_id=runs.thread_id, run_id=runs.id))
                try:
                    message_content = messages[0].content[0].text
                    app.logger.info(message_content.value)
                    pattern = re.compile(r"【\d+:\d+†\(?source\)?】")
                    filtered_message = pattern.sub("", message_content.value)
                    result = {"role": "assistant", "content": filtered_message}
                except IndexError:
                    result = {"role": "assistant", "content": "Saat ini saya tidak memiliki informasi yang diperlukan untuk menjawab pertanyaan Anda."}
        else:
            time.sleep(3)
            json_response = app.openai_client.chat.completions.create(model=chat_model,
                                                                      messages=chat_messages,
                                                                      max_tokens=max_resp_token, temperature=0.7, n=n)
            app.logger.info(json_response.choices[0].message)
            if has_named_params:
                if suggest:
                    choices = json_response.choices
                    messages = [i.message for i in choices]
                    json_formatted = []
                    for message in messages:
                        json_formatted.append({"role": "assistant", "content": message.content})
                    result = {"url": "", "message": json_formatted}
                else:
                    if use_video:
                        # TODO: to be implemented
                        result = {"url": url_for('download_file', name="test.mp4", _external=True),
                                  "message": {"role": "assistant", "content": json_response.choices[0].message.content}}
                    else:
                        result = {"role": "assistant", "content": json_response.choices[0].message.content}
            else:
                result = {"role": "assistant", "content": json_response.choices[0].message.content}
        if expression:
            exprr = expresso(text=result['content'])
            result['expression'] = exprr['expression']
        if predict_q:
            json_response_q = None
            if assistant_id:
                query_q = {
                    "role": "user",
                    "content": f"Berikan {predict_q} pertanyaan random yang akan saya ajukan sesuai topik asisten dalam bentuk json array"
                }
            else:
                query_q = {
                    "role": "user",
                    "content": f"Berikan {predict_q} pertanyaan lain yang akan saya ajukan berdasarkan percakapan kali ini dalam bentuk json array"
                }
            chat_messages.append(prune_message(result))
            chat_messages.append(query_q)
            if assistant_id:
                runs = app.openai_client.beta.threads.create_and_run_poll(
                    assistant_id=assistant_id,
                    thread={
                        "messages": chat_messages
                    }
                )
                if runs.status == "completed":
                    messages = list(app.openai_client.beta.threads.messages.list(thread_id=runs.thread_id, run_id=runs.id))
                    message_content = messages[0].content[0].text
                    app.logger.info(message_content.value)
                    pattern = re.compile(r"【\d+:\d+†\(?source\)?】")
                    filtered_message = pattern.sub("", message_content.value)
                    predict_q_arr = [
                        {
                            "role": "system",
                            "content": assistant.instructions
                        },
                        {
                            "role": "assistant",
                            "content": filtered_message
                        },
                        {
                            "role": "user",
                            "content": f"Ekstrak {predict_q} pertanyaan tersebut dalam bentuk json array"
                        }
                    ]
                    json_response_q = app.openai_client.chat.completions.create(
                        model=chat_model,
                        messages=predict_q_arr,
                        temperature=0.2,
                        response_format={"type": "json_object"}
                    )
            else:
                json_response_q = app.openai_client.chat.completions.create(model=chat_model,
                                                                            messages=chat_messages,
                                                                            max_tokens=max_resp_token,
                                                                            temperature=0.2,
                                                                            response_format={"type": "json_object"})
            if json_response_q:
                json_response_dict = json.loads(json_response_q.choices[0].message.content)
                if json_response_dict is not None:
                    if isinstance(json_response_dict, dict):
                        if len(json_response_dict) > 1:
                            qs = []
                            for q in json_response_dict.values():
                                qs.append(q)
                            json_response_dict = qs
                        else:
                            try:
                                first_key = next(iter(json_response_dict))
                                json_response_dict = json_response_dict[first_key]
                            except StopIteration:
                                json_response_dict = []
                    elif isinstance(json_response_dict, str):
                        json_response_dict = [json_response_dict]
                    result["predict_q"] = json_response_dict
    except openai.APITimeoutError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": e.message}, 408
    except openai.NotFoundError as e:
        app.logger.exception("error")
        result = {"status": "error", "message": json.loads(e.response.content)['error']['message']}, e.status_code
    except Exception:
        app.logger.exception("error")
        result = {"status": "error", "message": "Please try again"}, 405
    app.logger.info("Result: " + str(result))
    return json.dumps(result)


@app.route('/train', methods=['POST'])
def train():
    prev_model = "gpt-3.5-turbo"
    instructions = None
    if 'job_id' in request.form:
        return train_with_id(job_id=request.form['job_id'])
    elif 'train_file' in request.files:
        train_file = request.files['train_file']
        app.logger.info({"filename": train_file.filename})
        if 'instructions' in request.form:
            instructions = request.form['instructions']
        openai_file = None
        if train_file.filename.split('.')[1] == 'jsonl':
            openai_file = train_file.stream.read()
        elif train_file.filename.split('.')[1] == 'csv':
            openai_file = csv_to_jsonl(train_file.stream.read(), instructions)
        elif train_file.filename.split('.')[1] == 'json':
            openai_file = alpaca_to_chatgpt(train_file, instructions)
        if 'prev_model' in request.form:
            prev_model = request.form['prev_model']
        app.logger.info(f"Previous model: {prev_model}")
        if 'mock' not in request.form:
            f = app.openai_client.files.create(
                file=openai_file,
                purpose="fine-tune"
            )
            job = app.openai_client.fine_tuning.jobs.create(
                training_file=f.id,
                model=prev_model,
                hyperparameters={
                    "n_epochs": 5
                }
            )
            app.logger.info({"mock": "no", "status": job.status, "job_id": job.id})
            retval = {"status": job.status, "job_id": job.id}
            return retval
        else:
            app.logger.info({"mock": "yes", "status": "ok"})
            return {"status": "ok"}
    else:
        app.logger.error({"status": "error", "message": "Training file not found"})
        return {"status": "error", "message": "Training file not found"}


def train_with_id(job_id):
    try:
        time.sleep(3)
        job = app.openai_client.fine_tuning.jobs.retrieve(job_id)
        if job.fine_tuned_model is None:
            app.logger.info({"job_id": job_id, "status": job.status})
            return {"status": job.status}
        else:
            app.logger.info({"job_id": job_id, "status": job.status, "model_name": job.fine_tuned_model})
            return {"status": job.status, "model_name": job.fine_tuned_model}
    except Exception as error_print:
        app.logger.exception("error")
        return {"status": "Could not find job from id"}


@app.route('/assistant/create', methods=['POST'])
def assistant_create():
    model_name = "gpt-4o-mini"
    assistant_name = "Assistant"
    assistant_ins = "Please respond professionally and in a friendly manner, using the same language as the original request."
    if request.is_json:
        request_form = request.json
    else:
        request_form = request.form.copy()
    assistant_name = request_form.pop('name', assistant_name)
    assistant_ins = request_form.pop('instructions', assistant_ins)
    model_name = request_form.pop('model_name', model_name)
    vector_store_id = request_form.pop('vector_store_id', "")
    file_batch_id = ""
    try:
        temperature = float(request_form.pop('temperature', 1.0))
        if temperature < 0.0:
            temperature = 0.0
        elif temperature > 1.0:
            temperature = 1.0
    except ValueError:
        temperature = 1.0
    tool_resources = {"tool_resources": {"file_search": {"vector_store_ids": [vector_store_id]}}} \
        if vector_store_id \
        else {}
    try:
        time.sleep(3)
        assistant = app.openai_client.beta.assistants.create(
            name=assistant_name,
            instructions=assistant_ins,
            model=model_name,
            tools=[{"type": "file_search"}],
            temperature=temperature,
            **tool_resources,
            **request_form
        )
        if 'attachment1' in request.files and not vector_store_id:
            resp_att = assistant_att()
            retval = {}
            if resp_att['status'] == 'completed':
                resp_upd = assistant_update(assistant.id, resp_att['vector_store_id'])
                assistant_updated = "1" if resp_upd['status'] == 'ok' else "0"
            else:
                assistant_updated = "0"
                if 'vector_store_id' in resp_att:
                    retval['vector_store_id'] = resp_att['vector_store_id']
                if 'file_batch_id' in resp_att:
                    retval['file_batch_id'] = resp_att['file_batch_id']
            retval['status'] = "ok"
            retval['assistant_id'] = assistant.id
            retval['assistant_updated'] = assistant_updated
            return retval
        else:
            return {"status": "ok", "assistant_id": assistant.id, "assistant_updated": "1" if vector_store_id else "0"}
    except ValueError as e:
        app.logger.exception("error")
        return {"status": "error",
                "message": "Failed to create assistant, please check whether your parameters are correct"}
    except openai.NotFoundError as e:
        app.logger.exception("error")
        return {"status": "error", "message": json.loads(e.response.content)['error']['message']}, e.status_code
    except Exception:
        app.logger.exception("error")
        return {"status": "error", "message": "Failed to create assistant, please try again"}, 405


@app.route('/assistant/attachment', methods=['POST'])
def assistant_att():
    vector_store_id = request.form.get('vector_store_id', '')
    file_batch_id = request.form.get('file_batch_id', '')
    attachments: list[str] = []
    try:
        if not file_batch_id:
            if 'attachment1' not in request.files:
                return {"status": "error", "message": "No file for attachments"}
            else:
                has_attachments = True
                n = 1
                while has_attachments:
                    if f'attachment{n}' in request.files:
                        retf = app.openai_client.files.create(
                            file=(request.files[f'attachment{n}'].filename,
                                  request.files[f'attachment{n}'].read()),
                            purpose="assistants"
                        )
                        retf.filename = request.files[f'attachment{n}'].filename
                        attachments.append(retf.id)
                        n = n + 1
                    else:
                        has_attachments = False
            if vector_store_id:
                vector_store = app.openai_client.beta.vector_stores.retrieve(vector_store_id=vector_store_id)
            else:
                vector_store = app.openai_client.beta.vector_stores.create(
                    expires_after={
                        "anchor": "last_active_at",
                        "days": 365
                    }
                )
            file_batch = app.openai_client.beta.vector_stores.file_batches.create_and_poll(
                vector_store_id=vector_store.id,
                file_ids=attachments
            )
            return {"status": file_batch.status, "vector_store_id": vector_store.id, "file_batch_id": file_batch.id}
        else:
            file_batch = app.openai_client.beta.vector_stores.file_batches.retrieve(file_batch_id,
                                                                                    vector_store_id=vector_store_id)
            return {"status": file_batch.status}
    except Exception as e:
        app.logger.exception("error")
        return {"status": "error", "message": "Upload attachment failed, please try again"}


@app.route('/assistant/attachment/update', methods=['POST'])
def assistant_attachment_update():
    pass


@app.route('/assistant/update', methods=['POST'])
def assistant_update(aid=None, vid=None):
    try:
        request_form = request.form.copy()
        if aid is not None and vid is not None:
            assistant_id = aid
            vector_store_id = vid
        else:
            assistant_id = request_form.pop('assistant_id')
            vector_store_id = request_form.pop('vector_store_id', None)
        kwargs = {"assistant_id": assistant_id}
        if vector_store_id is not None:
            kwargs['tool_resources'] = {"file_search": {"vector_store_ids": [vector_store_id]}}
        if 'name' in request_form:
            kwargs['name'] = request_form.pop('name')
        if 'instructions' in request_form:
            kwargs['instructions'] = request_form.pop('instructions')
        time.sleep(3)
        app.openai_client.beta.assistants.update(**kwargs)
        return {"status": "ok"}
    except Exception as e:
        app.logger.exception("error")
        return {"status": "error", "message": "Update assistant failed, please try again"}


@app.route('/llama', methods=['POST'])
def llama():
    max_char_msg = 500
    max_resp_token = 600
    json_payload = request.get_json()
    if not json_payload:
        json_payload = []
    has_named_params = False
    if isinstance(json_payload, dict):
        has_named_params = 'payload' in json_payload
        if 'payload' in json_payload:
            json_payload = json_payload['payload']
            if isinstance(json_payload, dict):
                json_payload = [json_payload]
        else:
            json_payload = [json_payload]
    message = json_payload[-1]
    content = message['content']
    content_arr = content.split(" ")
    new_content_arr = content[:max_char_msg].split(" ")
    new_content_len = len(new_content_arr)
    arr = []
    for i in range(new_content_len):
        arr.append(content_arr[i])
    content = " ".join(arr)
    content = content + " Jawab dengan Bahasa Indonesia"
    try:
        json_request = {
            "model": "llama3.1",
            "prompt": content,
            "stream": False
        }
        r = requests.post("http://localhost:11434/api/generate", json=json_request)
        if r.status_code == 200:
            result = {
                "role": "assistant",
                "content": r.json()["response"]
            }
        else:
            result = {}, r.status_code
    except Exception as e:
        app.logger.exception("error")
        result = {"status": "error", "message": "Please try again"}, 405
    return result


@app.route('/speech', methods=['POST'])
def speech(text=""):
    time.sleep(3)
    if not text and 'text' not in request.form:
        audio_file = request.files.get('audio')
        res = app.openai_client.audio.transcriptions.create(
            model="whisper-1",
            file=(audio_file.filename, audio_file.stream.read())
        )
        return {"status": "ok", "message": res.text}
    elif 'text' in request.form or text:
        text = request.form['text'] if 'text' in request.form else text
        uu_id = str(uuid.uuid4())
        print(text)
        with app.openai_client.audio.speech.with_streaming_response.create(
            model="tts-1-hd",
            voice="echo",
            speed=0.8,
            input=text
        ) as res:
            res.stream_to_file(os.path.join(app.config['UPLOAD_FOLDER'], f"{uu_id}.mp3"))
        return download_file(f"{uu_id}.mp3")

@app.route('/expression', methods=['POST'])
def expresso(text=""):
    if not text:
        if 'text' in request.form:
            text = request.form['text']
        else:
            return {"status": "error", "message": "No text for expression"}
    try:
        response = app.openai_client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "user",
                    "content": f"What is the closest expression of this text, choose between happy, sad, indifferent, fear, anger, surprise, or disgust, output json with key 'expression':\n\n{text}\n\n"
                }
            ],
            response_format={"type": "json_object"}
        )
        response_message = response.choices[0].message.content
        return json.loads(response_message)
    except:
        return {"expression": "indifferent"}



# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8348, debug=True, ssl_context=ssl)

# See PyCharm help at https://www.jetbrains.com/help/pycharm/