app.py

import modal
import gradio as gr
import asyncio
import os
import logging
from agents.modal_orchestrator import ModalMovieSearchOrchestrator

# Настройка логирования
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Инициализация Modal App
app = modal.App("movie-plot-search")

# Глобальная инициализация оркестратора
orchestrator = ModalMovieSearchOrchestrator()

async def chat_interface(message: str, history: list) -> tuple:
    """ Основной интерфейс чата с Modal агентами """
    try:
        logger.info(f"Processing user message: {message[:50]}...")

        # Вызов оркестратора
        result = await orchestrator.process_user_input(message)
        logger.info(f"RESULT: {result}")
        
        status = result.get("status")
        response_parts = []

        # Логика формирования ответа
        if status == "insufficient_length":
            response_parts += [
                "**❗ Editor Feedback:**",
                result.get("message", ""),
                "\n---\nPlot description is too short (min 50 words). Please expand and try again."
            ]

        elif status == "search_completed":
            response_parts.append("**✅ Search completed!**")
            
            if (result.get("improved_plot") and result.get("improved_plot") != result.get("original_plot")):
                response_parts.append(f"**📝 Improved plot:** {result.get('improved_plot')}")

            if result.get("movie_overview"):
                response_parts.append(f"\n**🎬 Generated movie overview:**\n{result.get('movie_overview')}")

            response_parts.append("\n" + "=" * 60)
            response_parts.append("**🎯 EXPERT SYSTEM RECOMMENDATIONS**")
            response_parts.append("=" * 60)

            recommendations = result.get("recommendations", "")
            if isinstance(recommendations, dict):
                recommendations = recommendations.get("explanations", str(recommendations))
            
            if isinstance(recommendations, str) and recommendations:
                response_parts.append(recommendations)
            else:
                response_parts.append("No recommendations were generated.")

            # Метрики
            response_parts.append("\n" + "=" * 60)
            response_parts.append("**📊 PERFORMANCE METRICS**")
            response_parts.append("=" * 60)
            
            metrics = result.get("performance_metrics", {})
            if metrics:
                response_parts.append(f"🚀 **GPU Used:** {'✅ Yes' if metrics.get('using_gpu', False) else '❌ No'}")
                response_parts.append(f"⚡ **Search Time:** {metrics.get('search_time', 0):.3f}s")
                response_parts.append(f"🎬 **Movies Analyzed:** {result.get('total_analyzed', 0)}")

            response_parts.append("\n" + "=" * 60)
            response_parts.append("**🔄 Ready for the next search!**")

        elif status == "suggestion":
            response_parts.append("**💡 AI Plot Suggestion**")
            response_parts.append(result.get("message", ""))

        elif status == "awaiting_custom_plot":
            response_parts.append("**📝 Custom Plot Mode Activated**")
            response_parts.append(result.get("message", ""))

        elif status == "custom_plot_too_short":
            response_parts.append(result.get("message", "Your plot is too short."))

        elif status == "custom_plot_rejected":
            response_parts.append(result.get("message", "Your plot doesn't meet requirements."))

        elif status == "end_session":
            response_parts.append(result.get("message", "Thank you for using Movie Plot Search!"))

        elif status == "error":
            response_parts.append("**❌ System Error occurred:**")
            response_parts.append(result.get("message", "Unknown error"))
        
        else:
            response_parts.append(f"⚠️ Unhandled status: {status}")

        # Сборка ответа
        assistant_reply = "\n".join(response_parts)
        new_history = history + [
            {"role": "user", "content": message},
            {"role": "assistant", "content": assistant_reply}
        ]
        return new_history, ""

    except Exception as e:
        logger.error(f"Error in chat interface: {e}")
        new_history = history + [
            {"role": "user", "content": message},
            {"role": "assistant", "content": f"**❌ Unexpected error:** {e}"}
        ]
        return new_history, ""

def reset_chat():
    logger.info("Resetting chat session")
    orchestrator.reset_conversation()
    return [], ""

def get_session_info():
    try:
        summary = orchestrator.get_conversation_summary()
        return f"""**Hybrid Session Info:**
- ID: {summary.get('session_id', 'N/A')}
- Step: {summary.get('current_step', 'N/A')}
- Has Plot: {'✅' if summary.get('has_plot', False) else '❌'}
- Has Recommendations: {'✅' if summary.get('has_recommendations', False) else '❌'}
- Total Results: {summary.get('total_search_results', 0)}
        """
    except Exception as e:
        return f"Error getting session info: {e}"

def force_refresh_session_info():
    return get_session_info()

# --- 3. Создание интерфейса (Глобально!) ---
with gr.Blocks(title="🎬 Movie Plot Search") as demo:
    gr.Markdown("""
        # 🎬 Movie Plot Search Engine
        
        **🏗️ Architecture:**
             🖥️ **UI**: Hugging Face Spaces (Gradio);
             ⚡ **Agents**: Running on Modal Cloud (Serverless GPU);
             🤖 **LLM**: Nebius AI Studio API (Llama-3.3-70B-Instruct).

        ****How it works:**** *Describe a story, dream, or vibe (50+ words) in English. 5 AI Agents will collaborate to find 3 semantic matches.*
    """)

    with gr.Row():
        with gr.Column(scale=4):
            # Простой конструктор для максимальной совместимости
            chatbot = gr.Chatbot(
                value=[],
                height=600,
                label="🎬 Conversation with AI Agents",
                # type='messages' 
            )

            msg = gr.Textbox(
                placeholder="Describe a movie plot (50-100 words in English)...",
                label="Your message",
                lines=3,
                max_lines=5
            )

            with gr.Row():
                submit_btn = gr.Button("🚀 Submit", variant="primary", scale=2)
                clear_btn = gr.Button("🔄 Clear Chat", scale=1)

        with gr.Column(scale=1):
            gr.Markdown("### 🔍 How to use:\n1. Describe the plot (50+ words)\n2. Wait for agents to analyze\n3. Get expert recommendations")
            
            session_info = gr.Textbox(
                label="Session Info",
                value=get_session_info(),
                interactive=False,
                lines=5
            )
            refresh_btn = gr.Button("🔄 Refresh Info", size="sm")

    # Обработчики
    submit_btn.click(chat_interface, [msg, chatbot], [chatbot, msg]).then(get_session_info, None, session_info)
    msg.submit(chat_interface, [msg, chatbot], [chatbot, msg]).then(get_session_info, None, session_info)
    clear_btn.click(reset_chat, None, [chatbot, msg])
    refresh_btn.click(force_refresh_session_info, None, session_info)

# --- 4. Запуск (С ПРАВИЛЬНЫМИ ПАРАМЕТРАМИ) ---
if __name__ == "__main__":
    # ВАЖНО: 0.0.0.0 делает приложение видимым для Hugging Face
    # demo.launch(server_name="0.0.0.0", server_port=7860)
    demo.launch()