import os
import gradio as gr
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_parse import LlamaParse
import nest_asyncio

nest_asyncio.apply()

# --- CONFIGURAÇÃO VISUAL ---
theme_css = """
body { background-color: #0b0c10; color: #c5c6c7; font-family: 'Roboto', sans-serif; }
.header-container { display: flex; align-items: center; justify-content: center; gap: 15px; padding: 20px; border-bottom: 1px solid #1f2833; margin-bottom: 20px; }
.logo-img { height: 60px; width: auto; filter: drop-shadow(0 0 10px rgba(102, 252, 241, 0.5)); }
.brand-name { font-size: 32px; font-weight: bold; color: #66fcf1; letter-spacing: 4px; font-family: 'Impact', sans-serif; }
.chatbot-area { height: 500px !important; background-color: #1f2833; border: 1px solid #45a29e; border-radius: 10px; }
"""

global_query_engine = None

def processar_pdf(files, api_key_llama, api_key_openai):
    global global_query_engine
    
    # Limpa espaços em branco acidentais
    api_key_openai = api_key_openai.strip()
    api_key_llama = api_key_llama.strip()

    if not files: return "⚠️ Envie um PDF."
    if not api_key_openai.startswith("sk-"):
        return f"⚠️ Erro: A chave digitada não parece uma chave OpenAI válida. Ela começa com: {api_key_openai[:7]}..."

    # FORÇA AS CHAVES NO AMBIENTE
    os.environ["LLAMA_CLOUD_API_KEY"] = api_key_llama
    os.environ["OPENAI_API_KEY"] = api_key_openai
    
    try:
        # CONFIGURAÇÃO DIRETA (Ignora variáveis de ambiente globais do Hugging Face)
        Settings.llm = OpenAI(model="gpt-4o", api_key=api_key_openai)
        Settings.embed_model = OpenAIEmbedding(api_key=api_key_openai)
        
        parser = LlamaParse(result_type="markdown", language="pt")
        file_extractor = {".pdf": parser}
        filepaths = [f.name if hasattr(f, 'name') else f for f in files]
        
        documents = SimpleDirectoryReader(input_files=filepaths, file_extractor=file_extractor).load_data()
        index = VectorStoreIndex.from_documents(documents)
        global_query_engine = index.as_query_engine()
        
        return f"✅ CONECTADO! Chave detectada: {api_key_openai[:7]}***"
        
    except Exception as e:
        return f"❌ Erro Técnico: {str(e)}"

def responder(message, history):
    global global_query_engine
    if global_query_engine is None: return "⚠️ Sistema Offline. Configure acima."
    try:
        response = global_query_engine.query(message)
        return str(response)
    except Exception as e:
        return f"Erro: {str(e)}"

with gr.Blocks() as demo:
    with gr.Row(elem_classes="header-container"):
        if os.path.exists("logo.png"):
            gr.Image("logo.png", elem_classes="logo-img", show_label=False, show_download_button=False)
        gr.Markdown("<div class='brand-name'>COGNILINE</div>")

    with gr.Row():
        with gr.Column(scale=1, min_width=300):
            gr.Markdown("### ⚙️ Painel")
            txt_llama = gr.Textbox(label="LlamaCloud Key", type="password")
            # Adicionado o autocomplete="off" para o navegador não interferir
            txt_openai = gr.Textbox(label="OpenAI Key (sk-...)", type="password")
            file_up = gr.File(label="PDF", file_count="multiple", file_types=[".pdf"])
            btn_start = gr.Button("ATIVAR", variant="primary")
            lbl_status = gr.Textbox(label="Status", interactive=False)

        with gr.Column(scale=3):
            gr.ChatInterface(fn=responder, chatbot=gr.Chatbot(elem_classes="chatbot-area"))

    btn_start.click(processar_pdf, inputs=[file_up, txt_llama, txt_openai], outputs=lbl_status)

if __name__ == "__main__":
    demo.launch(css=theme_css, theme=gr.themes.Soft())