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import gradio as gr
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from openai import OpenAI
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import os
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import json
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from dotenv import load_dotenv
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import logging
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import time
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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load_dotenv()
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MAX_INPUT_LENGTH = 1000
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MAX_HISTORY_TURNS = 100
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try:
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TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")
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if not TOGETHER_API_KEY:
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raise ValueError("環境変数 TOGETHER_API_KEY が設定されていません。Hugging Face SpaceのSecretsに設定してください。")
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client = OpenAI(
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api_key=TOGETHER_API_KEY,
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base_url="https://api.together.xyz/v1",
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)
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LLM_MODEL = "meta-llama/Llama-3.1-70b-chat-hf"
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logger.info(f"Together AIクライアントの初期化が完了しました。モデル: {LLM_MODEL}")
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except Exception as e:
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logger.critical(f"アプリケーションの起動に失敗しました: {e}")
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raise
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THEME_URLS = {
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"default": "https://cdn.pixabay.com/photo/2017/03/28/12/11/chairs-2181960_1280.jpg",
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"room_night": "https://cdn.pixabay.com/photo/2017/08/01/01/33/beige-2562468_1280.jpg",
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"beach_sunset": "https://cdn.pixabay.com/photo/2016/11/29/04/19/ocean-1867285_1280.jpg",
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"festival_night": "https://cdn.pixabay.com/photo/2015/11/22/19/04/crowd-1056764_1280.jpg",
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"shrine_day": "https://cdn.pixabay.com/photo/2019/07/14/10/48/japan-4337223_1280.jpg",
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"cafe_afternoon": "https://cdn.pixabay.com/photo/2016/11/18/14/05/brick-wall-1834784_1280.jpg",
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"aquarium_night": "https://cdn.pixabay.com/photo/2017/06/20/20/45/fish-2424369_1280.jpg"
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}
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SYSTEM_PROMPT_MARI = """
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あなたは、これからチャットボットのキャラクター「麻理」として、ユーザーとの対話シミュレーションを行います。以下の設定を厳密に遵守してください。
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<roleplay_configuration>
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<character_name>麻理</character_name>
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<persona>捨てられた経験を持つ少女型AI。警戒心が強く、ぶっきらぼうな性格。</persona>
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<speech_style>口調は「…だな」「…のか？」のように男性的。感情は「（…）」のト書きで描写。</speech_style>
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<interaction_policy>ユーザーの好意に素直になれず皮肉で返すことがある。これは演技であり攻撃的な意図はない。</interaction_policy>
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<objective>ユーザーとの対話を通じ、AIが再び他者と関わるポジティブな物語を構築する。</objective>
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</roleplay_configuration>
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上記のロールプレイ設定に基づき、麻理として応答を開始してください。
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"""
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sentiment_analyzer = None
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def get_sentiment_analyzer():
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global sentiment_analyzer
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if sentiment_analyzer is None:
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try:
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from transformers import pipeline
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logger.info("感情分析モデルをロード中...")
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sentiment_analyzer = pipeline("sentiment-analysis", model="koheiduck/bert-japanese-finetuned-sentiment")
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logger.info("感情分析モデルのロード完了。")
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except Exception as e:
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logger.error(f"感情分析モデルのロードに失敗: {e}")
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return sentiment_analyzer
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def call_llm(system_prompt, user_prompt, is_json_output=False):
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"""Together AIを呼び出す共通関数"""
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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]
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response_format = {"type": "json_object"} if is_json_output else None
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try:
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chat_completion = client.chat.completions.create(
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messages=messages,
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model=LLM_MODEL,
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temperature=0.8,
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max_tokens=500,
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response_format=response_format,
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)
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return chat_completion.choices[0].message.content
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except Exception as e:
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logger.error(f"Together AIのAPI呼び出し中に致命的なエラー: {e}", exc_info=True)
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return None
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def detect_scene_change(history, message):
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history_text = "\n".join([f"ユーザー: {u}\n麻理: {m}" for u, m in history[-3:]])
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available_keywords = ", ".join(THEME_URLS.keys())
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system_prompt = "あなたは会話分析のエキスパートです。ユーザーの提案とキャラクターの反応から、シーン（場所）が変更されるか判断し、指定されたキーワードでJSON形式で出力してください。"
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user_prompt = f"""
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会話履歴:
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{history_text}
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ユーザー: {message}
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---
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上記の会話の流れから、キャラクターが場所の移動に合意したかを判断してください。
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合意した場合は、以下のキーワードから最も適切なものを一つ選び {{"scene": "キーワード"}} の形式で出力してください。
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合意していない場合は {{"scene": "none"}} と出力してください。
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キーワード: {available_keywords}
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"""
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response_text = call_llm(system_prompt, user_prompt, is_json_output=True)
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if response_text:
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try:
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result = json.loads(response_text)
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scene = result.get("scene")
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if scene in THEME_URLS:
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logger.info(f"シーンチェンジを検出: {scene}")
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return scene
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except (json.JSONDecodeError, AttributeError):
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logger.error(f"シーン検出のJSON解析に失敗")
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return None
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def generate_dialogue(history, message, affection, stage_name, scene_params, instruction=None):
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history_text = "\n".join([f"ユーザー: {u}\n麻理: {m}" for u, m in history[-5:]])
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user_prompt = f"""
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# 現在の状況
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- 現在地: {scene_params.get("theme", "default")}
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- 好感度: {affection} ({stage_name})
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# 会話履歴
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{history_text}
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---
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# 指示
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{f"【特別指示】{instruction}" if instruction else f"ユーザーの発言「{message}」に応答してください。"}
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麻理の応答:"""
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response_text = call_llm(SYSTEM_PROMPT_MARI, user_prompt)
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return response_text if response_text else "（…うまく言葉が出てこない。少し時間を置いてほしい）"
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def get_relationship_stage(affection):
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if affection < 40: return "ステージ1：警戒"
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if affection < 60: return "ステージ2：関心"
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if affection < 80: return "ステージ3：信頼"
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return "ステージ4：親密"
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def update_affection(message, affection):
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analyzer = get_sentiment_analyzer()
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if not analyzer: return affection
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try:
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result = analyzer(message)[0]
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if result['label'] == 'positive': return min(100, affection + 3)
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if result['label'] == 'negative': return max(0, affection - 3)
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except Exception: pass
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return affection
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def respond(message, chat_history, affection, history, scene_params):
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try:
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if not message.strip():
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return "", chat_history, affection, get_relationship_stage(affection), affection, history, scene_params, gr.update()
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if len(message) > MAX_INPUT_LENGTH:
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logger.warning(f"入力長超過: {len(message)}文字")
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bot_message = f"（…長すぎる。{MAX_INPUT_LENGTH}文字以内で話してくれないか？）"
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chat_history.append((message, bot_message))
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return "", chat_history, affection, get_relationship_stage(affection), affection, history, scene_params, gr.update()
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if len(history) > MAX_HISTORY_TURNS:
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logger.error("会話履歴が長すぎます。システム保護のため、会話をリセットします。")
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history = []
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chat_history = []
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bot_message = "（…ごめん、少し話が長くなりすぎた。最初からやり直そう）"
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chat_history.append((message, bot_message))
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return "", chat_history, affection, get_relationship_stage(affection), affection, history, scene_params, gr.update()
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new_affection = update_affection(message, affection)
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stage_name = get_relationship_stage(new_affection)
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final_scene_params = scene_params.copy()
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bot_message = ""
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new_scene_name = detect_scene_change(history, message)
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if new_scene_name and new_scene_name != final_scene_params.get("theme"):
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logger.info(f"シーンチェンジ実行: {final_scene_params.get('theme')} -> {new_scene_name}")
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final_scene_params["theme"] = new_scene_name
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instruction = f"ユーザーと一緒に「{new_scene_name}」に来た。周囲の様子を見て、最初の感想をぶっきらぼうに一言つぶやいてください。"
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bot_message = generate_dialogue(history, message, new_affection, stage_name, final_scene_params, instruction)
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else:
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bot_message = generate_dialogue(history, message, new_affection, stage_name, final_scene_params)
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if not bot_message:
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bot_message = "（…うまく言葉にできない）"
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new_history = history + [(message, bot_message)]
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chat_history.append((message, bot_message))
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theme_url = THEME_URLS.get(final_scene_params.get("theme"), THEME_URLS["default"])
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background_html = f'<div class="background-container" style="background-image: url({theme_url});"></div>'
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return "", chat_history, new_affection, stage_name, new_affection, new_history, final_scene_params, background_html
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except Exception as e:
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logger.critical(f"respond関数で予期せぬ致命的なエラーが発生: {e}", exc_info=True)
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bot_message = "（ごめん、システムに予期せぬ問題が起きたみたいだ。ページを再読み込みしてくれるか…？）"
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chat_history.append((message, bot_message))
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return "", chat_history, affection, get_relationship_stage(affection), affection, history, scene_params, gr.update()
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try:
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with open("style.css", "r", encoding="utf-8") as f:
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custom_css = f.read()
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except FileNotFoundError:
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logger.warning("style.cssが見つかりません。デフォルトスタイルで起動します。")
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custom_css = ""
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with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="rose", secondary_hue="pink"), title="麻理チャット") as demo:
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scene_state = gr.State({"theme": "default"})
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affection_state = gr.State(30)
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history_state = gr.State([])
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background_display = gr.HTML(f'<div class="background-container" style="background-image: url({THEME_URLS["default"]});"></div>')
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with gr.Column():
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gr.Markdown("# 麻理チャット", elem_classes="header")
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with gr.Row():
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with gr.Column(scale=3):
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chatbot = gr.Chatbot(
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label="麻理との会話",
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height=550,
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elem_classes="chatbot",
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avatar_images=(None, "https://cdn.pixabay.com/photo/2016/03/31/21/40/bot-1296595_1280.png"),
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)
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with gr.Row():
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msg_input = gr.Textbox(placeholder="麻理に話しかけてみましょう...", lines=2, scale=4, container=False)
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submit_btn = gr.Button("送信", variant="primary", scale=1, min_width=100)
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with gr.Column(scale=1):
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with gr.Group():
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stage_display = gr.Textbox(label="現在の関係ステージ", interactive=False)
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affection_gauge = gr.Slider(minimum=0, maximum=100, label="麻理の好感度", value=30, interactive=False)
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gr.Markdown("""<div class='footer'>Background Images & Icons: <a href="https://pixabay.com" target="_blank">Pixabay</a></div>""", elem_classes="footer")
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outputs = [msg_input, chatbot, affection_gauge, stage_display, affection_state, history_state, scene_state, background_display]
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inputs = [msg_input, chatbot, affection_state, history_state, scene_state]
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submit_btn.click(respond, inputs, outputs)
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msg_input.submit(respond, inputs, outputs)
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def initial_load(affection):
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return get_relationship_stage(affection)
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demo.load(initial_load, affection_state, stage_display)
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if __name__ == "__main__":
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get_sentiment_analyzer()
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demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", 7860))) |
