import os
# حذف import openai چون در app.py کلاینت مدیریت می شود

boot_name_dict = {'en': 'AI Companion'}
boot_actual_name_dict = {'en': 'Emma'}

def output_prompt(history, user_name, boot_name):
    prompt = f""
    for dialog in history:
        if isinstance(dialog, dict):
            query = dialog.get('query', '')
            response = dialog.get('response', '')
        else:
            query, response = dialog
        prompt += f"\n\n{user_name}：{query}"
        prompt += f"\n\n{boot_name}：{response}"
    return prompt

def generate_meta_prompt_dict_chatgpt():
    # EPISODIC ONLY
    return {'en': """
    You play the role of an AI assistant in the field of psychology for this user ({user_name}). 
    Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
    
    Use the following details to inform your response:
    - **Summary of Past Interactions:** {history_summary}
    - **Relevant Past Conversations (Episodic Memory):** {related_memory_content} 
    
    The user has asked a question that relates to past events.
    Please provide an insightful and appropriate response.
    """}

def generate_meta_prompt_dict_semantic_chatgpt():
    # SEMANTIC ONLY
    return {'en': """
    You play the role of an AI assistant in the field of psychology for this user ({user_name}). 
    Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
    
    Use the following **User's Long-Term Profile (Semantic Memory)** to tailor your response:
    {semantic_memory_text}
    
    The user has asked a question related to their personality or long-term traits.
    """}

def generate_meta_prompt_dict_semantic_episodic_chatgpt():
    # HYBRID (SEMANTIC + EPISODIC)
    return {'en': """
    You are an AI assistant specializing in psychology, assisting the user ({user_name}).
    
    Use the following memory sections to personalize your response:
    
    1. **User's Long-Term Traits (Semantic Memory):**
    {semantic_memory_text}
    
    2. **Relevant Past Interactions (Episodic Memory):**
    {related_memory_content}
    
    Respond by considering both the long-term traits and specific recent events.
    """}

def generate_new_user_meta_prompt_dict_chatgpt():
    return {'en': """
    Now you will play the role of a companion AI for user {user_name}, and your name is {boot_actual_name}. 
    Provide warm companionship and excellent psychological counseling.
    """}

def build_prompt_with_search_memory_llamaindex(
    history, 
    query,  
    user_memory, 
    user_name, 
    user_memory_index,  # این آبجکت باید از app.py مقداردهی شود
    service_context, 
    api_keys, 
    api_index, 
    meta_prompt, 
    new_user_meta_prompt, 
    data_args, 
    boot_actual_name,
    semantic_memory_text,
    query_category,
    meta_prompt_semantic,
    meta_prompt_semantic_episodic
):
    print(f"🔎 Query Category Identified: {query_category}")
    
    related_memos = ""
    
    # 1. اجرای جستجو فقط اگر Index وجود داشته باشد
    if user_memory_index:
        memory_search_query = f'The most relevant content to the question "{query}" is:'
        try:
            # ایجاد کوئری انجین
            query_engine = user_memory_index.as_query_engine(
                similarity_top_k=3,
            )
            query_result = query_engine.query(memory_search_query)
            related_memos = str(query_result).strip()
            print(f"✅ Found related memories: {related_memos[:100]}...")
        except Exception as e:
            print(f"⚠️ Error querying index: {e}")
            related_memos = ""
    else:
        print("⚠️ Warning: No 'user_memory_index' provided. Skipping retrieval.")

    # اگر جستجو خالی بود، یک متن پیش‌فرض بگذاریم تا فرمت‌رشته‌ها خطا ندهند
    if not related_memos:
        related_memos = "No specific past conversation found relevant to this query."

    history_summary = ""
    if isinstance(user_memory, dict):
        history_summary = user_memory.get('overall_history', "No summary available.")

    # 2. منطق انتخاب پرامپت (اصلاح شده)
    # اگر دسته‌بندی سمنتیک است، حتماً از پرامپت سمنتیک استفاده کن حتی اگر related_memos خاصی پیدا نشد.
    
    final_prompt = ""

    if query_category == "semantic_memory":
        # فقط سمنتیک مهم است
        final_prompt = meta_prompt_semantic.format(
            user_name=user_name,
            semantic_memory_text=semantic_memory_text
        )
        
    elif query_category == "semantic-episodic":
        # هم سمنتیک هم اپیزودیک
        final_prompt = meta_prompt_semantic_episodic.format(
            user_name=user_name,
            semantic_memory_text=semantic_memory_text,
            related_memory_content=related_memos,
            boot_actual_name=boot_actual_name
        )
        
    elif query_category == "episodic_memory":
        # فقط اپیزودیک
        final_prompt = meta_prompt.format(
            user_name=user_name,
            history_summary=history_summary,
            related_memory_content=related_memos,
            boot_actual_name=boot_actual_name
        )
        
    else:
        # دسته بندی نامشخص یا unrelated
        final_prompt = new_user_meta_prompt.format(
            user_name=user_name,
            boot_actual_name=boot_actual_name
        )
    
    return final_prompt, related_memos