utils/completion_reward.py

import logging
import json
import time
import io
import os
import re
import requests
import textwrap
import random
import hashlib
from datetime import datetime
from PIL import Image, ImageDraw, ImageFilter, ImageFont

import anthropic_bedrock
import gradio as gr
from opencc import OpenCC
from openai import OpenAI
from anthropic_bedrock import AnthropicBedrock, HUMAN_PROMPT, AI_PROMPT
from google.auth.transport.requests import Request
from google.oauth2.service_account import Credentials
from google import auth
from google.cloud import bigquery
from google.cloud import storage

SERVICE_ACCOUNT_INFO = os.getenv("GBQ_TOKEN")
SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]
service_account_info_dict = json.loads(SERVICE_ACCOUNT_INFO)

creds = Credentials.from_service_account_info(service_account_info_dict, scopes=SCOPES)

gbq_client = bigquery.Client(
    credentials=creds, project=service_account_info_dict["project_id"]
)
gcs_client = storage.Client(
    credentials=creds, project=service_account_info_dict["project_id"]
)


class CompletionReward:
    def __init__(self):
        self.player_backend_user_id = None
        self.player_name = None
        self.background_url = None
        self.player_selected_character = None
        self.player_selected_model = None
        self.player_selected_paragraph = None
        self.paragraph_openai = None
        self.paragraph_aws = None
        self.paragraph_google = None
        self.paragraph_mtk = None
        self.paragraph_ntu = None
        self.player_certificate_url = None
        self.openai_agent = OpenAIAgent()
        self.aws_agent = AWSAgent()
        self.google_agent = GoogleAgent()
        self.mtk_agent = MTKAgent()
        self.ntu_agent = NTUAgent()
        self.agents_responses = {}
        self.agent_list = [
            self.openai_agent,
            self.aws_agent,
            self.google_agent,
            self.mtk_agent,
            self.ntu_agent,
        ]
        self.shuffled_response_order = {}
        self.pop_response_order = []
        self.response_time_map = {}

    def get_llm_response_once(self, player_logs):
        if self.agent_list:
            # Randomly select and remove an agent from the list
            agent = self.agent_list.pop(random.randint(0, len(self.agent_list) - 1))
        else:
            return "No agents left", None

        story, response_time = agent.get_story(player_logs)
        self.agents_responses[agent.name] = story
        self.pop_response_order.append(agent.name)
        self.response_time_map[agent.name] = response_time

        if len(self.pop_response_order) == 5:
            self.shuffled_response_order = {
                str(index): agent for index, agent in enumerate(self.pop_response_order)
            }
            self.paragraph_openai = self.agents_responses["openai"]
            self.paragraph_aws = self.agents_responses["aws"]
            self.paragraph_google = self.agents_responses["google"]
            self.paragraph_mtk = self.agents_responses["mtk"]
            self.paragraph_ntu = self.agents_responses["ntu"]

        return [(None, story)]

    def set_player_name(self, player_name, player_backend_user_id):
        self.player_backend_user_id = player_backend_user_id
        self.player_name = player_name

    def set_background_url(self, background_url):
        self.background_url = background_url

    def set_player_backend_user_id(self, player_backend_user_id):
        self.player_backend_user_id = player_backend_user_id

    def set_player_selected_character(self, player_selected_character):
        character_map = {
            "露米娜": "0",
            "索拉拉": "1",
            "薇丹特": "2",
            "蔚藍": "3",
            "紅寶石": "4",
        }
        self.player_selected_character = player_selected_character
        self.player_selected_model = self.shuffled_response_order[
            character_map[player_selected_character]
        ]
        self.player_selected_paragraph = self.get_paragraph_by_model(
            self.player_selected_model
        )

    def get_paragraph_by_model(self, model):
        return getattr(self, f"paragraph_{model}", None)

    def create_certificate(self):
        image_url = self.openai_agent.get_background()
        self.set_background_url(image_url)
        source_file = ImageProcessor.generate_reward(
            image_url,
            self.player_name,
            self.player_selected_paragraph,
            self.player_backend_user_id,
        )

        public_url = self.upload_blob_and_get_public_url(
            "mes_completion_rewards", source_file, f"2023_mes/{source_file}"
        )
        self.player_certificate_url = public_url

        return gr.Image(public_url, visible=True, elem_id="certificate")

    def to_dict(self):
        return {
            "player_backend_user_id": self.player_backend_user_id,
            "player_name": self.player_name,
            "background_url": self.background_url,
            "player_selected_model": self.player_selected_model,
            "player_selected_paragraph": self.player_selected_paragraph,
            "paragraph_openai": self.paragraph_openai,
            "paragraph_aws": self.paragraph_aws,
            "paragraph_google": self.paragraph_google,
            "paragraph_mtk": self.paragraph_mtk,
            "paragraph_ntu": self.paragraph_ntu,
            "response_time_openai": self.response_time_map["openai"],
            "response_time_aws": self.response_time_map["aws"],
            "response_time_google": self.response_time_map["google"],
            "response_time_mtk": self.response_time_map["mtk"],
            "response_time_ntu": self.response_time_map["ntu"],
            "player_certificate_url": self.player_certificate_url,
            "created_at": datetime.now(),
        }

    def insert_data_into_bigquery(self, client, dataset_id, table_id, rows_to_insert):
        table_ref = client.dataset(dataset_id).table(table_id)
        table = client.get_table(table_ref)

        errors = client.insert_rows(table, rows_to_insert)

        if errors:
            logging.info("Errors occurred while inserting rows:")
            for error in errors:
                print(error)
        else:
            logging.info(f"Inserted {len(rows_to_insert)} rows successfully.")

    def complete_reward(
        self,
    ):
        insert_row = self.to_dict()
        self.insert_data_into_bigquery(
            gbq_client, "streaming_log", "log_mes_completion_rewards", [insert_row]
        )
        logging.info(
            f"Player {insert_row['player_backend_user_id']} rendered successfully."
        )

        with open("./data/completion_reward_issue_status.json") as f:
            completion_reward_issue_status_dict = json.load(f)

        completion_reward_issue_status_dict[
            insert_row["player_backend_user_id"]
        ] = self.player_certificate_url

        with open("./data/completion_reward_issue_status.json", "w") as f:
            json.dump(completion_reward_issue_status_dict, f)

    def upload_blob_and_get_public_url(
        self, bucket_name, source_file_name, destination_blob_name
    ):
        """Uploads a file to the bucket and makes it publicly accessible."""
        # Initialize a storage client
        bucket = gcs_client.bucket(bucket_name)
        blob = bucket.blob(destination_blob_name)

        # Upload the file
        blob.upload_from_filename(source_file_name)

        # The public URL can be used to directly access the uploaded file via HTTP
        public_url = blob.public_url

        logging.info(f"File {source_file_name} uploaded to {destination_blob_name}.")

        return public_url


class OpenAIAgent:
    def __init__(self):
        self.name = "openai"
        self.temperature = 0.8
        self.frequency_penalty = 0
        self.presence_penalty = 0
        self.max_tokens = 2048

    def get_story(self, user_log):
        system_prompt = """
            我正在舉辦一個學習型的活動，我為學生設計了一個獨特的故事機制，每天每個學生都會收到屬於自己獨特的冒險紀錄，現在我需要你協助我將這些冒險紀錄，製作成一段冒險故事，請
            - 以「你」稱呼學生
            - 可以裁減內容以將內容限制在 1024 個 token 內
            - 試著合併故事記錄成一段連貫、有吸引力的故事
            - 請使用 zh_TW
            - 請直接回覆故事內容，不需要回覆任何訊息
        """

        user_log = f"""
            ```{user_log}
            ```
        """

        messages = [
            {
                "role": "system",
                "content": f"{system_prompt}",
            },
            {
                "role": "user",
                "content": f"{user_log}",
            },
        ]

        client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        response = None

        retry_attempts = 0
        while retry_attempts < 5:
            start_time = time.time()
            try:
                response = client.chat.completions.create(
                    model="gpt-4-1106-preview",
                    messages=messages,
                    temperature=self.temperature,
                    max_tokens=self.max_tokens,
                    frequency_penalty=self.frequency_penalty,
                    presence_penalty=self.presence_penalty,
                )
                chinese_converter = OpenCC("s2tw")
                self.openai_response_time = time.time() - start_time
                return chinese_converter.convert(response.choices[0].message.content), self.openai_response_time

            except Exception as e:
                retry_attempts += 1
                logging.error(f"OpenAI Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)

        self.openai_response_time = time.time() - start_time
        return '星際夥伴短時間內寫了太多故事，需要休息一下，請稍後再試，或是選擇其他星際夥伴的故事。', self.openai_response_time

    def get_background(self):
        client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        image_url = None

        retry_attempts = 0
        while retry_attempts < 5:
            try:
                logging.info("Generating image...")
                response = client.images.generate(
                    model="dall-e-3",
                    prompt="Create an image in a retro Ghibli style, with a focus on a universe theme. The artwork should maintain the traditional hand-drawn animation look characteristic of Ghibli and with vibrant color. Imagine a scene set in outer space or a fantastical cosmic environment, rich with vibrant and varied color palettes to capture the mystery and majesty of the universe. The background should be detailed, showcasing stars, planets, and nebulae, blending the Ghibli style's nostalgia and emotional depth with the awe-inspiring aspects of space. The overall feel should be timeless, merging the natural wonder of the cosmos with the storytelling and emotional resonance typical of the retro Ghibli aesthetic. Soft lighting and gentle shading should be used to enhance the dreamlike, otherworldly quality of the scene.",
                    size="1024x1024",
                    quality="standard",
                    n=1,
                )

                image_url = response.data[0].url
                return image_url

            except Exception as e:
                retry_attempts += 1
                logging.error(f"DALLE Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)  # exponential backoff


class AWSAgent:
    def __init__(self):
        self.name = "aws"

    def get_story(self, user_log):
        system_prompt = """
            我正在舉辦一個學習型的活動，我為學生設計了一個獨特的故事機制，每天每個學生都會收到屬於自己獨特的冒險紀錄，現在我需要你協助我將這些冒險紀錄，製作成一段冒險故事，請
            - 以「你」稱呼學生
            - 可以裁減內容以將內容限制在 1024 個 token 內
            - 試著合併故事記錄成一段連貫、有吸引力的故事
            - 請使用 zh_TW
            - 請直接回覆故事內容，不需要回覆任何訊息
        """

        user_log = f"""
            ```{user_log}
            ```
        """
        client = AnthropicBedrock(
            aws_access_key=os.getenv("AWS_ACCESS_KEY"),
            aws_secret_key=os.getenv("AWS_SECRET_KEY"),
            aws_region="us-west-2",
        )

        retry_attempts = 0
        while retry_attempts < 5:
            try:
                start_time = time.time()
                completion = client.completions.create(
                    model="anthropic.claude-v2",
                    max_tokens_to_sample=2048,
                    prompt=f"{anthropic_bedrock.HUMAN_PROMPT}{system_prompt}，以下是我的故事紀錄```{user_log}``` {anthropic_bedrock.AI_PROMPT}",
                )
                chinese_converter = OpenCC("s2tw")

                self.aws_response_time = time.time() - start_time
                return chinese_converter.convert(completion.completion), self.aws_response_time

            except Exception as e:
                retry_attempts += 1
                logging.error(f"AWS Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)

        self.aws_response_time = time.time() - start_time
        return '星際夥伴短時間內寫了太多故事，需要休息一下，請稍後再試，或是選擇其他星際夥伴的故事。', self.aws_response_time


class GoogleAgent:
    from google.cloud import aiplatform
    from vertexai.preview.generative_models import GenerativeModel

    SERVICE_ACCOUNT_INFO = os.getenv("GBQ_TOKEN")
    service_account_info_dict = json.loads(SERVICE_ACCOUNT_INFO)
    SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]

    creds = Credentials.from_service_account_info(
        service_account_info_dict, scopes=SCOPES
    )
    aiplatform.init(
        project="junyiacademy",
        service_account=service_account_info_dict,
        credentials=creds,
    )

    gemini_pro_model = GenerativeModel("gemini-pro")

    def __init__(self):
        self.name = "google"

    def get_story(self, user_log):
        system_prompt = """
                    我正在舉辦一個學習型的活動，我為學生設計了一個獨特的故事機制，每天每個學生都會收到屬於自己獨特的冒險紀錄，現在我需要你協助我將這些冒險紀錄，製作成一段冒險故事，請
                    - 以「你」稱呼學生
                    - 可以裁減內容以將內容限制在 1024 個 token 內
                    - 試著合併故事記錄成一段連貫、有吸引力的故事
                    - 請使用 zh_TW
                    - 請直接回覆故事內容，不需要回覆任何訊息
                """

        user_log = f"""
            ```{user_log}
            ```
        """

        retry_attempts = 0
        while retry_attempts < 5:
            try:
                start_time = time.time()
                logging.info("Google Generating response...")
                model_response = self.gemini_pro_model.generate_content(
                    f"{system_prompt}, 以下是我的冒險故事 ```{user_log}```"
                )

                chinese_converter = OpenCC("s2tw")
                self.google_response_time = time.time() - start_time
                return chinese_converter.convert(
                    model_response.candidates[0].content.parts[0].text
                ), self.google_response_time

            except Exception as e:
                retry_attempts += 1
                logging.error(f"Google Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)

        self.google_response_time = time.time() - start_time
        return '星際夥伴短時間內寫了太多故事，需要休息一下，請稍後再試，或是選擇其他星際夥伴的故事。', self.google_response_time


class MTKAgent:
    def __init__(self):
        self.name = "mtk"

    def get_story(self, user_log):
        system_prompt = """
            我正在舉辦一個學習型的活動，我為學生設計了一個獨特的故事機制，每天每個學生都會收到屬於自己獨特的冒險紀錄，現在我需要你協助我將這些冒險紀錄，製作成一段冒險故事，請
            - 以「你」稱呼學生
            - 可以裁減內容以將內容限制在 1024 個 token 內
            - 試著合併故事記錄成一段連貫、有吸引力的故事
            - 請使用 zh_TW
            - 請直接回覆故事內容，不需要回覆任何訊息
        """

        user_log = f"""
            ```{user_log}
            ```
        """

        BASE_URL = "http://35.229.245.251:8008/v1"
        TOKEN = os.getenv("MTK_TOKEN")
        MODEL_NAME = "model7-c-chat"
        TEMPERATURE = 1
        MAX_TOKENS = 1024
        TOP_P = 0
        PRESENCE_PENALTY = 0
        FREQUENCY_PENALTY = 0
        message = f"{system_prompt}, 以下是我的冒險故事 ```{user_log}```"
        url = os.path.join(BASE_URL, "chat/completions")
        headers = {
            "accept": "application/json",
            "Authorization": f"Bearer {TOKEN}",
            "Content-Type": "application/json",
        }
        data = {
            "model": MODEL_NAME,
            "messages": str(message),
            "temperature": TEMPERATURE,
            "n": 1,
            "max_tokens": MAX_TOKENS,
            "stop": "",
            "top_p": TOP_P,
            "logprobs": 0,
            "echo": False,
            "presence_penalty": PRESENCE_PENALTY,
            "frequency_penalty": FREQUENCY_PENALTY,
        }

        retry_attempts = 0
        while retry_attempts < 5:
            try:
                start_time = time.time()
                response = requests.post(
                    url, headers=headers, data=json.dumps(data)
                ).json()
                response_text = response["choices"][0]["message"]["content"]

                matched_contents = re.findall("```(.*?)```", response_text, re.DOTALL)

                # Concatenate all extracted contents
                extracted_content = "\n".join(matched_contents).strip()

                chinese_converter = OpenCC("s2tw")
                self.mtk_response_time = time.time() - start_time
                if extracted_content:
                    return chinese_converter.convert(extracted_content), self.mtk_response_time
                else:
                    return chinese_converter.convert(response_text), self.mtk_response_time

            except Exception as e:
                retry_attempts += 1
                logging.error(f"MTK Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)

        self.mtk_response_time = time.time() - start_time
        return '星際夥伴短時間內寫了太多故事，需要休息一下，請稍後再試，或是選擇其他星際夥伴的故事。', self.mtk_response_time

class NTUAgent:
    def __init__(self):
        self.name = "ntu"

    def get_story(self, user_log):
        system_prompt = """
            我正在舉辦一個學習型的活動，我為學生設計了一個獨特的故事機制，每天每個學生都會收到屬於自己獨特的冒險紀錄，現在我需要你協助我將這些冒險紀錄，製作成一段冒險故事，請
            - 以「你」稱呼學生
            - 可以裁減內容以將內容限制在 1024 個 token 內
            - 試著合併故事記錄成一段連貫、有吸引力的故事
            - 請使用 zh_TW
            - 請直接回覆故事內容，不需要回覆任何訊息
        """

        user_log = f"""
            ```{user_log}
            ```
        """
        messages = [
            {
                "role": "system",
                "content": f"{system_prompt}",
            },
            {
                "role": "user",
                "content": f"{user_log}",
            },
        ]

        url = 'http://api.twllm.com:20002/v1/chat/completions'
        data = {
            "model": "yentinglin/Taiwan-LLM-13B-v2.0-chat",
            "messages": messages,
            "temperature": 0.7,
            "top_p": 1,
            "n": 1,
            "max_tokens": 2048,
            "stop": ["string"],
            "stream": False,
            "presence_penalty": 0,
            "frequency_penalty": 0,
            "user": "string",
            "best_of": 1,
            "top_k": -1,
            "ignore_eos": False,
            "use_beam_search": False,
            "stop_token_ids": [0],
            "skip_special_tokens": True,
            "spaces_between_special_tokens": True,
            "add_generation_prompt": True,
            "echo": False,
            "repetition_penalty": 1,
            "min_p": 0
        }

        headers = {
            'accept': 'application/json',
            'Content-Type': 'application/json'
        }

        retry_attempts = 0
        while retry_attempts < 5:
            try:
                start_time = time.time()
                response = requests.post(url, headers=headers, data=json.dumps(data)).json()
                response_text = response["choices"][0]["message"]["content"]
                matched_contents = re.findall("```(.*?)```", response_text, re.DOTALL)

                # Concatenate all extracted contents
                extracted_content = "\n".join(matched_contents).strip()

                chinese_converter = OpenCC("s2tw")
                self.ntu_response_time = time.time() - start_time
                logging.warning(f"NTU response time: {self.ntu_response_time}")
                if extracted_content:
                    return chinese_converter.convert(extracted_content), self.ntu_response_time
                else:
                    return chinese_converter.convert(response_text), self.ntu_response_time

            except Exception as e:
                retry_attempts += 1
                logging.error(f"NTU Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)

        self.ntu_response_time = time.time() - start_time
        return '星際夥伴短時間內寫了太多故事，需要休息一下，請稍後再試，或是選擇其他星際夥伴的故事。', self.ntu_response_time

class ImageProcessor:
    @staticmethod
    def draw_shadow(
        image, box, radius, offset=(10, 10), shadow_color=(0, 0, 0, 128), blur_radius=5
    ):
        shadow_image = Image.new("RGBA", image.size, (0, 0, 0, 0))
        shadow_draw = ImageDraw.Draw(shadow_image)
        shadow_box = [
            box[0] + offset[0],
            box[1] + offset[1],
            box[2] + offset[0],
            box[3] + offset[1],
        ]
        shadow_draw.rounded_rectangle(shadow_box, fill=shadow_color, radius=radius)
        shadow_image = shadow_image.filter(ImageFilter.GaussianBlur(blur_radius))
        image.paste(shadow_image, (0, 0), shadow_image)

    @staticmethod
    def generate_reward(url, player_name, paragraph, player_backend_user_id):
        retry_attempts = 0
        while retry_attempts < 5:
            try:
                response = requests.get(url)
                break
            except requests.RequestException as e:
                retry_attempts += 1
                logging.error(f"Attempt {retry_attempts}: {e}")
                time.sleep(1 * retry_attempts)  # exponential backoff

        image_bytes = io.BytesIO(response.content)
        img = Image.open(image_bytes)

        tmp_img = Image.new("RGBA", img.size, (0, 0, 0, 0))
        draw = ImageDraw.Draw(tmp_img)

        # Draw the text
        title_font = ImageFont.truetype("NotoSansTC-Bold.ttf", 34)
        body_font = ImageFont.truetype("NotoSansTC-Light.ttf", 14)

        # Calculate space required by the paragraph
        paragraph_height = 0
        for line in paragraph.split("\n"):
            wrapped_lines = textwrap.wrap(line, width=63)
            for wrapped_line in wrapped_lines:
                _, _, _, line_height = draw.textbbox(
                    (0, 0), wrapped_line, font=body_font
                )
                paragraph_height += line_height + 10

        # Draw the box
        padding = 40
        left, right = 50, img.width - 50
        box_height = min(800, paragraph_height + padding)
        top = (img.height - box_height) // 2
        bottom = (img.height + box_height) // 2
        border_radius = 20

        # Draw the rounded rectangle
        fill_color = (255, 255, 255, 200)
        draw.rounded_rectangle(
            [left, top, right, bottom],
            fill=fill_color,
            outline=None,
            radius=border_radius,
        )

        img.paste(Image.alpha_composite(img.convert("RGBA"), tmp_img), (0, 0), tmp_img)

        draw = ImageDraw.Draw(img)

        # Title text
        title = f"光束守護者 - {player_name} 的冒險故事"
        title_x, title_y = left + 20, top + 20  # Adjust padding as needed
        draw.text((title_x, title_y), title, font=title_font, fill="black")

        # Paragraph text with newlines
        body_x, body_y = left + 20, title_y + 60  # Adjust position as needed

        for line in paragraph.split("\n"):
            wrapped_lines = textwrap.wrap(line, width=63)
            for wrapped_line in wrapped_lines:
                draw.text((body_x, body_y), wrapped_line, font=body_font, fill="black")
                body_y += 25

        # Save the image with the text

        def get_md5_hash(text):
            return hashlib.md5(text.encode("utf-8")).hexdigest()

        updated_image_path = f"certificate_{get_md5_hash(player_backend_user_id)}.png"
        img.save(updated_image_path)

        return updated_image_path