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.player_certificate_url = None self.openai_agent = OpenAIAgent() self.aws_agent = AWSAgent() self.google_agent = GoogleAgent() self.mtk_agent = MTKAgent() self.shuffled_response_order = {} self.paragraph_map = { "openai": self.paragraph_openai, "aws": self.paragraph_aws, "google": self.paragraph_google, "mtk": self.paragraph_mtk, } def get_llm_response(self, player_logs): agents_responses = { "openai": self.openai_agent.get_story(player_logs), "aws": self.aws_agent.get_story(player_logs), "google": self.google_agent.get_story(player_logs), "mtk": self.mtk_agent.get_story(player_logs), } self.paragraph_openai = agents_responses["openai"] self.paragraph_aws = agents_responses["aws"] self.paragraph_google = agents_responses["google"] self.paragraph_mtk = agents_responses["mtk"] response_items = list(agents_responses.items()) random.shuffle(response_items) self.shuffled_response_order = { str(index): agent for index, (agent, _) in enumerate(response_items) } shuffled_responses = tuple(response for _, response in response_items) return ( [(None, shuffled_responses[0])], [(None, shuffled_responses[1])], [(None, shuffled_responses[2])], [(None, shuffled_responses[3])], ) 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", } 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, "player_certificate_url": self.player_certificate_url, "created_at_date": datetime.now().date(), } 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.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: 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") return chinese_converter.convert(response.choices[0].message.content) except Exception as e: retry_attempts += 1 logging.error(f"OpenAI Attempt {retry_attempts}: {e}") time.sleep(1 * retry_attempts) 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 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: 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") return chinese_converter.convert(completion.completion) except Exception as e: retry_attempts += 1 logging.error(f"AWS Attempt {retry_attempts}: {e}") time.sleep(1 * retry_attempts) 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 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: 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") return chinese_converter.convert( model_response.candidates[0].content.parts[0].text ) except Exception as e: retry_attempts += 1 logging.error(f"Google Attempt {retry_attempts}: {e}") time.sleep(1 * retry_attempts) class MTKAgent: 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: 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") if extracted_content: return chinese_converter.convert(extracted_content) else: return chinese_converter.convert(response_text) except Exception as e: retry_attempts += 1 logging.error(f"MTK Attempt {retry_attempts}: {e}") time.sleep(1 * retry_attempts) 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) # Fonts title_font = ImageFont.truetype("NotoSansTC-Bold.ttf", 34) body_font = ImageFont.truetype("NotoSansTC-Light.ttf", 14) # Text contents title = f"光束守護者 - {player_name} 的冒險故事" paragraph = "Your long paragraph text goes here..." # Replace with your paragraph # Calculate text size left, right = 50, img.width - 50 title_x, title_y = left + 20, 20 # Title position body_x, body_y = left + 20, title_y + 60 # Body position # Calculate space required by the paragraph paragraph_height = 0 for line in paragraph.split("\n"): wrapped_lines = textwrap.wrap(line, width=60) for wrapped_line in wrapped_lines: line_height = draw.textsize(wrapped_line, font=body_font)[1] paragraph_height += line_height + 25 # Calculate box height and top, bottom position padding = 40 # Additional padding box_height = max(600, paragraph_height + padding) # Minimum height or paragraph height top = (img.height - box_height) // 2 bottom = (img.height + box_height) // 2 # Draw the rounded rectangle border_radius = 20 fill_color = (255, 255, 255, 200) draw.rounded_rectangle([left, top, right, bottom], fill=fill_color, outline=None, radius=border_radius) # Draw the title text draw.text((title_x, top + 20), title, font=title_font, fill="black") # Draw the paragraph text body_y = top + 60 + title_font.getsize(title)[1] for line in paragraph.split("\n"): wrapped_lines = textwrap.wrap(line, width=60) for wrapped_line in wrapped_lines: draw.text((body_x, body_y), wrapped_line, font=body_font, fill="black") body_y += draw.textsize(wrapped_line, font=body_font)[1] + 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