Update app.py

app.py

@@ -13,15 +13,10 @@ from langchain.memory import ConversationBufferMemory
 from transformers import AutoTokenizer, pipeline
 
 # ===================================================================
-# CONFIGURAÇÃO RADICAL DE HARDWARE
+# CONFIGURAÇÃO DE COMBATE
 # ===================================================================
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 TORCH_DTYPE = torch.bfloat16 if DEVICE == "cuda" else torch.float32
-MAX_MEMORY = "16GB" if DEVICE == "cpu" else None
-
-# ===================================================================
-# LISTA DE MODELOS OTIMIZADOS
-# ===================================================================
 LLM_MODELS = {
     "TinyLlama-1.1B-Chat": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
     "Phi-2": "microsoft/phi-2",
@@ -30,38 +25,43 @@ LLM_MODELS = {
 }
 
 # ===================================================================
-# NÚCLEO DO SISTEMA
+# NÚCLEO DE OPERAÇÕES ESPECIAIS
 # ===================================================================
-class DocumentProcessor:
+class TacticalDocumentProcessor:
     @staticmethod
-    def load_and_split(files, chunk_size=512, chunk_overlap=64):
-        """Carrega e processa documentos com paralelismo extremo"""
+    def neutralize_documents(files, chunk_size=512, chunk_overlap=64):
+        """Operação de desmantelamento de documentos hostis"""
+        if not files:
+            raise ValueError("ALVO NÃO IDENTIFICADO")
+        
         try:
             loaders = [PyPDFLoader(file.name) for file in files]
-            return [page for loader in loaders for page in loader.load_and_split(
-                RecursiveCharacterTextSplitter(
-                    chunk_size=chunk_size,
-                    chunk_overlap=chunk_overlap,
-                    separators=["\n\n", "\n", " ", ""]
-                )
-            )]
+            splitter = RecursiveCharacterTextSplitter(
+                chunk_size=chunk_size,
+                chunk_overlap=chunk_overlap,
+                separators=["\n\n", "\n", "\. ", " ", ""]
+            )
+            return [page for loader in loaders for page in loader.load_and_split(splitter)]
         except Exception as e:
-            raise RuntimeError(f"FALHA CRÍTICA NO PROCESSAMENTO: {str(e)}")
+            raise RuntimeError(f"FALHA NA OPERAÇÃO: {str(e)}")
 
-class VectorDBManager:
+class VectorStrikeSystem:
     @staticmethod
-    def create(splits):
-        """Cria vetorização com aceleração de hardware"""
+    def deploy_vector_db(splits):
+        """Implante imediato de sistema de vetorização"""
+        if not splits:
+            raise ValueError("NENHUMA INTELECÇÃO DISPONÍVEL")
+        
         return Chroma.from_documents(
             documents=splits,
             embedding=HuggingFaceEmbeddings(),
-            persist_directory="./chroma_db"
+            persist_directory="./combat_db"
         )
 
-class LLMEngine:
+class LLMWeaponsSystem:
     @staticmethod
-    def initialize(model_name, temp=0.7, max_tokens=512):
-        """Inicialização agressiva do modelo com otimizações de baixo nível"""
+    def activate_weapon(model_name, temp=0.7, max_tokens=512):
+        """Ativação de armamento cognitivo"""
         try:
             tokenizer = AutoTokenizer.from_pretrained(LLM_MODELS[model_name])
             
@@ -80,86 +80,104 @@ class LLMEngine:
             
             return HuggingFacePipeline(pipeline=pipe)
         except KeyError:
-            raise ValueError("MODELO NÃO SUPORTADO!")
+            raise ValueError("ARMA NÃO CATALOGADA")
         except Exception as e:
-            raise RuntimeError(f"FALHA NUCLEAR NO MODELO: {str(e)}")
+            raise RuntimeError(f"FALHA NO SISTEMA DE ARMAMENTO: {str(e)}")
 
 # ===================================================================
 # INTERFACE DE COMBATE
 # ===================================================================
-def create_war_interface():
-    with gr.Blocks(theme=gr.themes.Soft(), title="⚔️ PDF Assault v1.0") as warzone:
-        state = gr.State({"db": None, "llm": None})
+def deploy_combat_interface():
+    with gr.Blocks(theme=gr.themes.Soft(), title="🔥 WARBOT v2.0") as interface:
+        state = gr.State({
+            "db": None,
+            "llm": None,
+            "doc_status": False,
+            "model_status": False
+        })
         
-        # Zona de Upload
+        # Zona de Controle Tático
         with gr.Row(variant="panel"):
-            file_upload = gr.Files(label="DOCUMENTOS ALVO", file_types=[".pdf"])
-            process_btn = gr.Button("ATAQUE!", variant="stop")
-        
-        # Controles Táticos
-        with gr.Row(variant="compact"):
-            model_selector = gr.Dropdown(list(LLM_MODELS.keys()), label="ARMA PRINCIPAL", value="TinyLlama-1.1B-Chat")
-            temp_slider = gr.Slider(0, 1, 0.7, label="POTÊNCIA DE FOGO")
-            deploy_btn = gr.Button("DEPLOY MODELO", variant="primary")
-        
-        # Campo de Batalha
-        chatbot = gr.Chatbot(height=600, label="ZONA DE OPERAÇÕES")
-        msg_input = gr.Textbox(label="COMANDO DE ATAQUE", placeholder="Insira o alvo...")
+            with gr.Column(scale=1):
+                file_upload = gr.Files(label="CARREGAMENTO DE ALVOS", file_types=[".pdf"])
+                process_btn = gr.Button("INICIAR PROCESSAMENTO", variant="stop")
+                process_log = gr.Textbox(label="RELATÓRIO DE PROCESSAMENTO", interactive=False)
+            
+            with gr.Column(scale=1):
+                model_selector = gr.Dropdown(list(LLM_MODELS.keys()), label="SELECIONE O ARMAMENTO", value="TinyLlama-1.1B-Chat")
+                temp_slider = gr.Slider(0, 1, 0.7, label="NÍVEL DE AGRESSIVIDADE")
+                deploy_btn = gr.Button("ATIVAR ARMAMENTO", variant="primary")
+                deploy_log = gr.Textbox(label="STATUS DO ARMAMENTO", interactive=False)
         
-        # Sistema de Logs
-        combat_log = gr.Textbox(label="RELATÓRIO DE COMBATE", interactive=False)
+        # Campo de Batalha Principal
+        chatbot = gr.Chatbot(height=650, label="ZONA DE ENGENHARIA COGNITIVA")
+        msg_input = gr.Textbox(label="INSIRA COMANDO DE ATAQUE", placeholder="Aguardando ordens...")
+        clear_btn = gr.Button("LIMPAR CAMPO DE BATALHA")
 
-        # ===== Operações Militares =====
-        @process_btn.click(inputs=[file_upload], outputs=[state, combat_log])
-        def assault_documents(files):
+        # ===== OPERAÇÕES TÁTICAS =====
+        @process_btn.click(inputs=[file_upload], outputs=[state, process_log])
+        def execute_processing(files):
             try:
-                splits = DocumentProcessor.load_and_split(files)
-                db = VectorDBManager.create(splits)
-                return {"db": db, "llm": None}, "✅ DOCUMENTOS CAPTURADOS!"
+                splits = TacticalDocumentProcessor.neutralize_documents(files)
+                db = VectorStrikeSystem.deploy_vector_db(splits)
+                return {
+                    "db": db,
+                    "llm": None,
+                    "doc_status": True,
+                    "model_status": False
+                }, "✅ ALVOS PROCESSADOS COM SUCESSO"
             except Exception as e:
-                return state.value, f"☠️ FALHA CATACLÍSMICA: {str(e)}"
+                return state.value, f"☠️ FALHA CRÍTICA: {str(e)}"
 
-        @deploy_btn.click(inputs=[model_selector, temp_slider, state], outputs=[state, combat_log])
-        def deploy_model(model, temp, current_state):
+        @deploy_btn.click(inputs=[model_selector, temp_slider, state], outputs=[state, deploy_log])
+        def deploy_weapon(model, temp, current_state):
             try:
-                llm = LLMEngine.initialize(model, temp)
+                if not current_state["doc_status"]:
+                    raise RuntimeError("ALVOS NÃO PROCESSADOS! EXECUTE A FASE 1")
+                
+                llm = LLMWeaponsSystem.activate_weapon(model, temp)
                 current_state["llm"] = ConversationalRetrievalChain.from_llm(
                     llm=llm,
-                    retriever=current_state["db"].as_retriever(),
+                    retriever=current_state["db"].as_retriever(search_kwargs={"k": 3}),
                     memory=ConversationBufferMemory(memory_key="chat_history", return_messages=True),
                     return_source_documents=True
                 )
-                return current_state, f"🚀 {model} PRONTO PARA COMBATE!"
+                current_state["model_status"] = True
+                return current_state, f"🚀 {model} PRONTO PARA ENGAGEMENT"
             except Exception as e:
-                return current_state, f"💥 FALHA NO DEPLOY: {str(e)}"
+                return current_state, f"💥 FALHA NO ARMAMENTO: {str(e)}"
 
         @msg_input.submit(inputs=[msg_input, chatbot, state], outputs=[msg_input, chatbot])
-        def execute_combat(command, history, state):
-            if not state["llm"]:
-                return command, history + [(command, "⚠️ MODELO NÃO DEPLOYADO!")]
+        def execute_engagement(command, history, state):
+            if not state["model_status"]:
+                return command, history + [(command, "⚠️ ARMAMENTO NÃO ATIVADO")]
             
             try:
                 result = state["llm"]({"question": command, "chat_history": history})
-                response = f"🎯 RESPOSTA:\n{result['answer']}\n\n"
-                response += "📌 INTEL:\n" + "\n".join(
-                    f"Página {doc.metadata['page']+1}: {doc.page_content[:75]}..." 
+                intel_report = "\n".join(
+                    f"🔍 Pg {doc.metadata['page']+1}: {doc.page_content[:100]}..." 
                     for doc in result["source_documents"][:3]
                 )
-                return "", history + [(command, response)]
+                return "", history + [
+                    (command, f"🎯 RESPOSTA:\n{result['answer']}\n\n📡 INTELIGÊNCIA:\n{intel_report}")
+                ]
             except Exception as e:
-                return command, history + [(command, f"☢️ FALHA CRÍTICA: {str(e)}")]
+                return command, history + [(command, f"☢️ FALHA OPERACIONAL: {str(e)}")]
+
+        @clear_btn.click(inputs=[], outputs=[chatbot])
+        def clear_battlefield():
+            return []
 
-    return warzone
+    return interface
 
 # ===================================================================
 # INICIALIZAÇÃO DO SISTEMA
 # ===================================================================
 if __name__ == "__main__":
-    interface = create_war_interface()
-    interface.launch(
+    combat_system = deploy_combat_interface()
+    combat_system.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        share=False,
-        auth=("admin", "combat123"),
-        show_error=True
+        auth=("commander", "tactical123"),
+        share=True
     )