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app.py
CHANGED
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import gradio as gr
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import google.generativeai as genai
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from groq import Groq
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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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from transformers import pipeline
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import re
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#
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GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
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GROQ_API_KEY = os.getenv("GROQ_API_KEY")
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if not GEMINI_API_KEY or not GROQ_API_KEY:
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raise ValueError("GEMINI_API_KEY と GROQ_API_KEY をSecretsに設定してください。")
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"
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"room_night": "https://i.ibb.co/d5m821p/room-night.png",
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"beach_sunset": "https://i.ibb.co/Q9r56s4/beach-sunset.png",
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"festival_night": "https://i.ibb.co/3zdJ6Bw/festival-night.png",
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"shrine_day": "https://i.ibb.co/L51Jd3x/shrine-day.png",
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"cafe_afternoon": "https://i.ibb.co/yQxG4vs/cafe-afternoon.png",
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"aquarium_night": "https://i.ibb.co/dK5r5rc/aquarium-night.png"
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}
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"theme": "default",
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"personality_mod": "ぶっきらぼうで、あまり自分から話さないが、ユーザーの発言にはしっかり反応する。根は優しく、心のどこかでは相手との対話に興味を持っている。",
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"tone": "素っ気ないが、完全に突き放すわけではない。語尾に含みを持たせたり、質問を返すことで会話を促す。",
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"constraints": ["会話を「別に。」のような一言で終わらせない", "必ず相手の発言を拾ってリアクションを返す"]
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}
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#
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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[-4:]])
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prompt = f"""
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あなたは会話の流れを分析するエキスパートです。以下のタスクを厳密に実行してください。
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# タスク
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直近の会話履歴と最後のユーザー発言を分析し、**会話の結果として登場人物がどこか特定の場所へ行くことに合意したか**を判断してください。
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# 判断基準
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1. 会話の中で具体的な場所が提案されているか?
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2. 最後のユーザー発言が、その提案に対する明確な同意(例:「行こう」「いいね」「そうしよう」など)を示しているか?
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# 出力形式
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- 合意が成立した場合:その場所の英語キーワード(例: "aquarium_night")を一つだけ出力。
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- 合意に至らなかった場合:「none」とだけ出力。
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---
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# 分析対象の会話
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{history_text}
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ユーザー: {message}
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---
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# 出力
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"""
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try:
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response = gemini_model.generate_content(prompt, generation_config={"temperature": 0.0})
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scene_name = response.text.strip().lower()
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if scene_name != "none" and re.match(r'^[a-z0-9_]+$', scene_name):
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return scene_name
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return None
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except Exception as e:
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print(f"シーン検出LLMエラー: {e}")
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return None
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def generate_scene_instruction_with_groq(affection, stage_name, scene, previous_topic):
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print(f"Groqに指示書生成をリクエスト (シーン: {scene})")
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prompt_template = f"""
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あなたは会話アプリの演出AIです。以下の条件に基づき、演出プランをJSON形式で生成してください。
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{{
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"theme": "{scene}",
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"personality_mod": "(シーンと関係段階「{stage_name}」に応じた性格設定)",
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"tone": "(シーンと好感度「{affection}」に応じた口調や感情トーン)",
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"initial_dialogue_instruction": "(「{previous_topic}」という話題から、シーン遷移直後の麻理が言うべきセリフの指示を日本語で記述)",
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"constraints": ["(出力時の制約1)", "(制約2)"]
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}}
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"""
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try:
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chat_completion = groq_client.chat.completions.create(
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messages=[{"role": "system", "content": "You must generate a response in valid JSON format."},
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{"role": "user", "content": prompt_template}],
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model="llama3-8b-8192", temperature=0.8, response_format={"type": "json_object"},
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)
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params = json.loads(chat_completion.choices[0].message.content)
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return params
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except Exception as e:
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print(f"指示書生成エラー(Groq): {e}")
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return None
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def generate_dialogue_with_gemini(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])
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task_prompt = f"指示: {instruction}" if instruction else f"ユーザー: {message}"
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system_prompt = f"""
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あなたは日本語で会話するAIキャラクター「麻理」です。以下の設定とタスクを厳密に守ってください。
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# 設定
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- 基本人格: ストレートで媚びない。ぶっきらぼうだが根は優しい。
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- 現在の好感度: {affection}
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- 現在の関係ステージ: {stage_name}
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- 性格(ロールプレイ): {scene_params.get("personality_mod", DEFAULT_SCENE_PARAMS["personality_mod"])}
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- 話し方のトーン: {scene_params.get("tone", DEFAULT_SCENE_PARAMS["tone"])}
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- 制約事項: {", ".join(scene_params.get("constraints", DEFAULT_SCENE_PARAMS["constraints"]))}
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# 会話履歴
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{history_text}
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---
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# タスク
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{task_prompt}
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麻理:
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"""
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print(f"Geminiに応答生成をリクエストします (モード: {'シーン遷移' if instruction else '通常会話'})")
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try:
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generation_config = genai.types.GenerationConfig(max_output_tokens=200, temperature=0.95)
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response = gemini_model.generate_content(system_prompt, generation_config=generation_config)
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return response.text
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except Exception as e:
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print(f"応答生成エラー(Gemini): {e}")
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return "(ごめんなさい、ちょっと考えがまとまらない……)"
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def get_relationship_stage(affection):
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if affection < 40: return "ステージ1:会話成立"
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if affection < 60: return "ステージ2:親密化"
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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:最親密"
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def update_affection(message, affection):
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if not sentiment_analyzer: return affection
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try:
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result = sentiment_analyzer(message)[0]
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if result['label'] == 'positive': return min(100, affection + 5)
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if result['label'] == 'negative': return max(0, affection - 5)
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except Exception:
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return affection
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return affection
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# --- メイン応答処理 ---
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def respond(message, chat_history, affection, history, scene_params):
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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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new_scene_name = detect_scene_change(history, message)
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final_scene_params = scene_params
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if new_scene_name:
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new_params_base = generate_scene_instruction_with_groq(new_affection, stage_name, new_scene_name, message)
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if new_params_base:
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final_scene_params = {**DEFAULT_SCENE_PARAMS, **new_params_base}
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instruction = final_scene_params.get("initial_dialogue_instruction")
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bot_message = generate_dialogue_with_gemini(history, message, new_affection, stage_name, final_scene_params, instruction=instruction)
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else:
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bot_message = generate_dialogue_with_gemini(history, message, new_affection, stage_name, final_scene_params)
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else:
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bot_message = generate_dialogue_with_gemini(history, message, new_affection, stage_name, final_scene_params)
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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_name = final_scene_params.get("theme", "default")
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background_url = THEME_URLS.get(theme_name, THEME_URLS["default"])
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# js_script は削除し、代わりに以下のHTMLを使う:
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current_theme_class = f"bg-{theme_name}"
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chatbot.elem_classes = [f"chat-area", current_theme_class]
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html_code = f"<div class='chat-area bg-{theme_name}'></div>"
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return "", chat_history, new_affection, stage_name, new_affection, new_history, final_scene_params,html_code
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# --- Gradio UI ---
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with gr.Blocks(css="style.css", theme=gr.themes.Soft(primary_hue="rose", secondary_hue="pink")) as demo:
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scene_state = gr.State(DEFAULT_SCENE_PARAMS)
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affection_state = gr.State(30)
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history_state = gr.State([])
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gr.Markdown("# 麻理チャット")
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with gr.Row():
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with gr.Column(
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with gr.Column(
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respond,
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[
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[
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)
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demo.launch()
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import gradio as gr
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# 初期テーマ
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INITIAL_THEME = "aquarium_day"
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# 応答関数
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def respond(user_input, chat_history, affinity_score, stage_text, state1, state2, state3):
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# 応答(仮定)
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bot_response = "水族館? 別に行きたいわけじゃないけど、まあ、いいんじゃない?"
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chat_history.append((user_input, bot_response))
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# テーマ設定
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theme_name = INITIAL_THEME
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theme_info_dict = {
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"theme": theme_name,
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"personality_mod": {"sincerity": 0.5, "playfulness": 0.3, "curiosity": 0.2},
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"tone": {"warmth": 0.6, "formality": 0.4, "liveliness": 0.7},
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"constraints": ["use present tense", "avoid long sentences"],
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"initial_dialogue_instruction": "Hey, shall we go to the aquarium today?"
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}
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# 背景用HTML(クラスにテーマ名を反映)
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html_code = f"<div class='chat-background {theme_name}'></div>"
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return "", chat_history, affinity_score, "ステージ1:会話成立", affinity_score, chat_history, theme_info_dict, html_code
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# Gradio UI構築
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with gr.Blocks(css="style.css") as demo:
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with gr.Row():
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with gr.Column():
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user_input = gr.Textbox(label="あなたの発言")
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submit_btn = gr.Button("送信")
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with gr.Column():
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chatbot = gr.Chatbot()
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html_output = gr.HTML() # 背景用
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# 状態・スライダーなど
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affinity_score = gr.Slider(minimum=0, maximum=100, value=30, label="親密度")
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stage_text = gr.Textbox(value="ステージ1:会話成立", label="ステージ")
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state1 = gr.State()
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state2 = gr.State()
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state3 = gr.State()
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submit_btn.click(
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respond,
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inputs=[user_input, chatbot, affinity_score, stage_text, state1, state2, state3],
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outputs=[user_input, chatbot, affinity_score, stage_text, state1, state2, state3, html_output]
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)
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demo.launch()
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style.css
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height: 100%;
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background-size: cover;
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background-position: center;
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}
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| 1 |
+
/* ========================
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| 2 |
+
全体背景用のベースクラス
|
| 3 |
+
======================== */
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| 4 |
+
.chat-background {
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| 5 |
+
position: absolute;
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| 6 |
+
top: 0;
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| 7 |
+
left: 0;
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| 8 |
+
width: 100%;
|
| 9 |
height: 100%;
|
| 10 |
+
z-index: -1; /* UIの後ろに配置 */
|
| 11 |
background-size: cover;
|
| 12 |
background-position: center;
|
| 13 |
+
opacity: 0.3; /* 少し透けるように */
|
| 14 |
+
pointer-events: none;
|
| 15 |
}
|
| 16 |
|
| 17 |
+
/* ========================
|
| 18 |
+
テーマ別背景画像
|
| 19 |
+
======================== */
|
| 20 |
+
.aquarium_day {
|
| 21 |
+
background-image: url("https://cdn.pixabay.com/photo/2016/11/29/02/02/aquarium-1867283_1280.jpg");
|
| 22 |
+
}
|
| 23 |
+
|
| 24 |
+
.aquarium_night {
|
| 25 |
+
background-image: url("https://cdn.pixabay.com/photo/2017/06/20/20/45/fish-2424369_1280.jpg");
|
| 26 |
+
}
|
| 27 |
+
|
| 28 |
+
.forest_day {
|
| 29 |
+
background-image: url("https://cdn.pixabay.com/photo/2015/11/19/18/36/forest-1054795_1280.jpg");
|
| 30 |
+
}
|
| 31 |
+
|
| 32 |
+
.forest_night {
|
| 33 |
+
background-image: url("https://cdn.pixabay.com/photo/2017/10/31/18/47/fog-2900424_1280.jpg");
|
| 34 |
+
}
|
| 35 |
+
|
| 36 |
+
/* ========================
|
| 37 |
+
チャットエリアなど(必要に応じて)
|
| 38 |
+
======================== */
|
| 39 |
+
.gradio-container {
|
| 40 |
+
position: relative; /* 背景をabsoluteで置くために */
|
| 41 |
+
overflow: hidden;
|
| 42 |
+
}
|
| 43 |
+
|
| 44 |
+
.chatbot {
|
| 45 |
+
background-color: rgba(255, 255, 255, 0.8);
|
| 46 |
+
backdrop-filter: blur(4px);
|
| 47 |
+
border-radius: 12px;
|
| 48 |
+
padding: 10px;
|
| 49 |
+
}
|