Create app.py

app.py

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+import os
+import gradio as gr
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, load_index_from_storage
+from llama_parse import LlamaParse
+import shutil
+
+# --- CONFIGURAÇÃO VISUAL (CSS INDUSTRIAL DARK) ---
+theme_css = """
+body { background-color: #0b0c10; color: #c5c6c7; font-family: 'Roboto', sans-serif; }
+gradio-app { background-color: #0b0c10 !important; }
+/* Cabeçalho */
+.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; }
+/* Chat e Botões */
+.chatbot-area { height: 500px !important; overflow-y: auto; background-color: #1f2833; border: 1px solid #45a29e; border-radius: 10px; }
+button.primary { background-color: #45a29e !important; color: #0b0c10 !important; font-weight: bold; }
+input { background-color: #1f2833 !important; color: white !important; border: 1px solid #45a29e !important; }
+label { color: #66fcf1 !important; }
+"""
+
+# --- VARIÁVEIS GLOBAIS ---
+INDEX_STORAGE = "./storage"
+global_query_engine = None
+
+# --- FUNÇÃO 1: PROCESSAR O PDF ---
+def processar_pdf(files, api_key_llama, api_key_openai):
+    global global_query_engine
+    
+    if not files:
+        return "⚠️ Por favor, envie um arquivo PDF primeiro."
+    
+    # Configura chaves (Pegando dos inputs ou do sistema)
+    os.environ["LLAMA_CLOUD_API_KEY"] = api_key_llama
+    os.environ["OPENAI_API_KEY"] = api_key_openai
+    
+    try:
+        # Configura o Parser
+        parser = LlamaParse(result_type="markdown", language="pt")
+        file_extractor = {".pdf": parser}
+
+        # Lê os arquivos
+        documents = SimpleDirectoryReader(input_files=[f.name for f in files], file_extractor=file_extractor).load_data()
+
+        # Cria o índice
+        index = VectorStoreIndex.from_documents(documents)
+        
+        # Salva o motor de busca na memória
+        global_query_engine = index.as_query_engine()
+        
+        return f"✅ Sucesso! {len(files)} manual(is) processado(s). O sistema COGNILINE está online."
+    
+    except Exception as e:
+        return f"❌ Erro ao processar: {str(e)}"
+
+# --- FUNÇÃO 2: CHAT ---
+def responder(message, history):
+    global global_query_engine
+    
+    if global_query_engine is None:
+        return "⚠️ O sistema está offline. Por favor, faça upload do manual na aba lateral primeiro."
+    
+    try:
+        response = global_query_engine.query(message)
+        return str(response)
+    except Exception as e:
+        return f"Erro técnico: {str(e)}"
+
+# --- INTERFACE GRÁFICA (GRADIO BLOCKS) ---
+with gr.Blocks(css=theme_css, theme=gr.themes.Soft()) as demo:
+    
+    # 1. Cabeçalho com Logo
+    with gr.Row(elem_classes="header-container"):
+        # Se você subir o arquivo logo.png junto com o app.py, ele aparece aqui
+        if os.path.exists("logo.png"):
+            gr.Image("logo.png", elem_classes="logo-img", show_label=False, show_download_button=False, container=False)
+        gr.Markdown("<div class='brand-name'>COGNILINE</div>")
+
+    # 2. Layout Principal
+    with gr.Row():
+        
+        # Coluna da Esquerda (Configurações)
+        with gr.Column(scale=1, min_width=300):
+            gr.Markdown("### ⚙️ Configuração do Sistema")
+            
+            # Inputs de Chave (Para segurança, pedimos aqui ou usamos Secrets)
+            txt_llama_key = gr.Textbox(label="LlamaCloud API Key", type="password", placeholder="Cole sua chave llx-...")
+            txt_openai_key = gr.Textbox(label="OpenAI API Key", type="password", placeholder="Cole sua chave sk-...")
+            
+            # Upload
+            file_upload = gr.File(label="Upload de Manuais (PDF)", file_count="multiple", file_types=[".pdf"])
+            btn_process = gr.Button("INICIAR SISTEMA", variant="primary")
+            status_output = gr.Textbox(label="Status", interactive=False)
+
+        # Coluna da Direita (Chat)
+        with gr.Column(scale=3):
+            chatbot = gr.ChatInterface(
+                fn=responder,
+                chatbot=gr.Chatbot(elem_classes="chatbot-area", bubble_full_width=False),
+                textbox=gr.Textbox(placeholder="Digite sua pergunta técnica aqui...", container=False, scale=7),
+                theme="soft",
+                submit_btn="ENVIAR",
+                retry_btn=None,
+                undo_btn=None,
+                clear_btn="Limpar"
+            )
+
+    # 3. Ações
+    btn_process.click(processar_pdf, inputs=[file_upload, txt_llama_key, txt_openai_key], outputs=status_output)
+
+# Lançar
+if __name__ == "__main__":
+    demo.launch()