import os

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

import json

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

openai_key = os.environ.get("OPENAI_KEY", "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")

openai.api_key = openai_key
app.chat_messages = [
    {"role": "system",
     "content": "Please respond professionally and in a friendly manner, using the same language as the original request."}
]
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 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)
        print(chat_messages)
        json_response = openai.ChatCompletion.create(model="gpt-3.5-turbo-1106",
                                                     messages=chat_messages,
                                                     response_format={ "type": "json_object" }
                                                     )
        print(json_response.choices[0]["message"]["content"])
        return json.loads(json_response.choices[0]["message"]["content"])
    except Exception as error_print:
        app.logger.error(error_print)
        result = {}, 405

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)
        print(chat_messages)
        json_response = openai.ChatCompletion.create(
            model="gpt-4-vision-preview",
            messages=chat_messages,
            max_tokens=500
        )
        return json_response.choices[0]["message"]
    except Exception as error_print:
        app.logger.error(error_print)
        result = {}, 405


@app.route('/gpt', methods=['POST'])
def gpt():
    chat_messages = app.chat_messages.copy()
    use_video = False
    suggest = False
    summarize = False
    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
    if isinstance(json_payload, dict):
        has_named_params = 'payload' in json_payload
        if 'payload' in json_payload:
            if 'num_choices' in json_payload:
                num_choices = 5 if json_payload['num_choices'] > 5 else json_payload['num_choices']
            if 'use_video' in json_payload:
                use_video = json_payload['use_video'] == "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 = 4096
                else:
                    chat_messages = app.chat_messages.copy()
                json_payload['payload'][-1]['content'] = f"Please summarize this article:\n" + json_payload['payload'][-1]['content']
            else:
                chat_messages = app.chat_messages.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 = openai.ChatCompletion.create(model="gpt-3.5-turbo",  # GPT-3.5 Turbo engine
                                                     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:
            chat_messages = app.chat_messages.copy()
            json_payload = [json_payload]
    json_payload = json_payload[-7:]
    for message in json_payload:
        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])
        message['content'] = " ".join(arr)
        chat_messages.append(message)
    app.logger.info(chat_messages)
    result = {}
    try:
        n = num_choices
        json_response = openai.ChatCompletion.create(model="gpt-3.5-turbo",  # GPT-3.5 Turbo engine
                                                     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]
                result = {"url": "", "message": messages}
            elif use_video:
                # TODO: to be implemented
                result = {"url":  url_for('download_file', name="test.mp4", _external=True), "message": json_response.choices[0].message}
            else:
                result = {"url": "", "message": json_response.choices[0].message}
        else:
            result = json_response.choices[0].message
    except Exception as error_print:
        app.logger.error(error_print)
        result = {}, 405
    return result


# 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/