interface.py

# interface.py
import gradio as gr
import uuid
from ai_logic import (
    responder_como_aldo,
    build_and_save_vector_store,
    MODELS,
    DEFAULT_MODEL,
    inicializar_sistema # <--- Import the initialization function
)

css_customizado = """
.gradio-container {
    max-width: 1400px !important;
    margin: 0 auto;
    width: 99%;
    height: 100vh !important;
    display: flex;
    flex-direction: column;
    overflow: hidden;
}

.main-content {
    display: flex;
    flex-direction: column;
    height: 100vh;
    overflow: hidden;
    flex-shrink: 0;
}

.titulo-principal {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
    color: white !important;
    padding: 10px !important;
    border-radius: 10px !important;
    margin-bottom: 10px !important;
    text-align: center !important;
    flex-shrink: 0;
}

.chat-area {
    flex: 1;
    display: flex;
    flex-direction: column;
    overflow: hidden;
}

.chat-container {
    flex: 1;
    overflow-y: auto;
    margin-bottom: 10px;
}

.input-container {
    flex-shrink: 0;
    padding: 10px 0;
    display: flex;
    flex-direction: column;
    justify-content: center;
}

.additional-content {
    overflow-y: auto;
    padding-top: 20px;
}

.gr-textbox textarea {
    font-size: 14px !important;
    line-height: 1.5 !important;
}

.resposta-container {
    background-color: #ffffff !important;
    color: #1a1a1a !important;
    border: 1px solid #e0e0e0 !important;
    border-radius: 20px !important;
    padding: 20px !important;
    margin: 10px 0 !important;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.05) !important;
}

.resposta-container pre code {
    color: #1a1a1a !important;
    background-color: #f8f9fa !important;
}

.pergunta-container {
    background-color: #f0f8ff !important;
    border-radius: 8px !important;
    padding: 15px !important;
}

.modelo-dropdown {
    margin-bottom: 15px !important;
}

#entrada_usuario textarea {
    color: white !important;
    font-size: large !important;
    background-color: #1a1a1a !important;
    min-height: 60px !important;
}

.message-content {
    opacity: 1 !important;
    font-size: larger;
    color: white !important;
    background-color: #1a1a1a !important;
}

/* Responsivo */
@media (max-width: 768px) {
    .titulo-principal {
        padding: 10px !important;
    }
    #entrada_usuario textarea {
        min-height: 50px !important;
        font-size: 16px !important;
    }
}
#component-6{flex-grow: initial !important;}
#component-7{flex-grow: initial !important;}
.message-wrap.svelte-gjtrl6 .prose.chatbot.md {
    opacity: 1 !important;
}
"""

def criar_interface():
    with gr.Blocks(title="Dr. Aldo Henrique - API Externa", theme=gr.themes.Soft(), css=css_customizado) as interface:
        session_id_state = gr.State(str(uuid.uuid4()))  # Geração do session_id único

        with gr.Column(elem_classes="main-content"):
            gr.HTML("""
            <div class="titulo-principal">
                <h4 style="margin: 0;">🤖 iAldo - Converse com o <a href="https://aldohenrique.com.br/" style="color: white; text-decoration: underline;">Prof. Dr. Aldo Henrique</a></h4>
            </div>
            """)

            with gr.Column(elem_classes="chat-area"):
                with gr.Column(elem_classes="chat-container"):
                    chatbot = gr.Chatbot(
                        label="💬 Área do chat",
                        elem_id="chat",
                        height="100%"
                    )

                with gr.Column(elem_classes="input-container"):
                    with gr.Row():
                        # The choices for the dropdown now depend on the globally updated MODELS from ai_logic
                        modelo_select = gr.Dropdown(
                            choices=list(MODELS.keys()), # <--- Uses the MODELS dict from ai_logic
                            value=DEFAULT_MODEL,
                            label="🧠 Selecione o Modelo de Pensamento",
                            elem_classes="modelo-dropdown"
                        )
                    with gr.Row():
                        user_input = gr.Textbox(
                            show_label=False,
                            placeholder="Digite sua pergunta e pressione Enter ou clique em Enviar",
                            lines=2,
                            elem_id="entrada_usuario"
                        )
                        enviar_btn = gr.Button("Enviar", variant="primary")

        with gr.Column(elem_classes="additional-content"):
            with gr.Accordion("⚙️ Controle do Conhecimento (RAG)", open=False):
                status_rag = gr.Textbox(label="Status do Retreino", interactive=False)
                botao_retreinar = gr.Button("🔄 Atualizar Conhecimento do Blog", variant="stop")
                download_faiss_file = gr.File(label="Download do Índice FAISS", interactive=False, file_count="single", file_types=[".pkl"])
                download_urls_file = gr.File(label="Download das URLs Processadas", interactive=False, file_count="single", file_types=[".pkl"])

            with gr.Accordion("📚 Exemplos de Perguntas", open=False):
                gr.Examples(
                    examples=[
                        ["Como implementar uma lista ligada em C com todas as operações básicas?", DEFAULT_MODEL],
                        ["Qual a sua opinião sobre o uso de ponteiros em C++ moderno, baseada no seu blog?", "Mistral 7B (Mais acertivo)"], # Use label, not model name
                        ["Resuma o que você escreveu sobre machine learning no seu blog.", "Zephyr 7B (Meio Termo)"], # Use label, not model name
                    ],
                    inputs=[user_input, modelo_select]
                )

            with gr.Accordion("🔧 Status da API", open=False):
                # We can't directly show model status here without re-running tests.
                # For simplicity, we'll assume if the page loads, models are good.
                status_api = gr.Textbox(label="Status dos Modelos", interactive=False, lines=8, 
                                        value="Modelos carregados com sucesso! Verifique o console para detalhes.")


            with gr.Accordion("ℹ️ Informações", open=False):
                gr.Markdown("""
                ### Sobre o Dr. Aldo Henrique:
                - **Especialidade**: Linguagens C, Java, Desenvolvimento Web, Inteligência Artificial
                - **Conhecimento Adicional**: Conteúdo do blog aldohenrique.com.br

                ### Dicas para melhores respostas:
                - Faça perguntas específicas sobre o conteúdo do blog para ver o RAG em ação!
                - Peça resumos ou opiniões sobre temas que o professor aborda.
                """)

        # ✅ Função corrigida com uso de session_id
        def responder(chat_history, user_msg, modelo, session_id):
            if not user_msg.strip():
                return chat_history, ""

            chat_history = chat_history + [[user_msg, "Dr. Aldo Henrique está digitando..."]]
            yield chat_history, ""

            resposta_final = responder_como_aldo(session_id, user_msg, modelo)
            chat_history[-1][1] = resposta_final
            yield chat_history, ""

        # ✅ Botão e Enter usam o novo estado de sessão
        enviar_btn.click(
            fn=responder,
            inputs=[chatbot, user_input, modelo_select, session_id_state],
            outputs=[chatbot, user_input],
            show_progress=True
        )

        user_input.submit(
            fn=responder,
            inputs=[chatbot, user_input, modelo_select, session_id_state],
            outputs=[chatbot, user_input],
            show_progress=True
        )

        botao_retreinar.click(
            fn=build_and_save_vector_store,
            outputs=[status_rag, download_faiss_file, download_urls_file],
            show_progress=True
        )

        gr.HTML("""
        <script>
            window.addEventListener("load", function() {
                const textarea = document.querySelector("#entrada_usuario textarea");
                if (textarea) {
                    setTimeout(() => textarea.focus(), 100);
                }
            });
        </script>
        """)

    return interface

def configurar_interface():
    # Attempt to initialize the backend system and check for model availability
    if inicializar_sistema(): # <--- This is the key change
        return criar_interface() # Only create the Gradio interface if successful
    else:
        # If initialization fails, display an error page instead of the full interface
        return gr.HTML("<h1>Erro ao carregar a página: Não há modelos de IA suficientes disponíveis.</h1><p>Verifique o console para mais detalhes sobre os modelos e sua conexão com o Hugging Face.</p>")

# This part runs when you execute interface.py
if __name__ == "__main__":
    app = configurar_interface()
    app.launch()