Update app.py

app.py

@@ -5,6 +5,7 @@
 import os
 from pathlib import Path
 import pickle
+from typing import List, Dict, Any
 
 import gradio as gr
 import faiss
@@ -12,8 +13,6 @@ from sentence_transformers import SentenceTransformer
 from openai import OpenAI
 
 # ========= NVIDIA API =========
-# Em local: defina NV_API_KEY ou NVIDIA_API_KEY no ambiente.
-# Em Hugging Face Spaces: crie um "Repository secret" chamado NVIDIA_API_KEY.
 NV_API_KEY = os.environ.get("NVIDIA_API_KEY") or os.environ.get("NV_API_KEY")
 if not NV_API_KEY:
     raise RuntimeError(
@@ -87,13 +86,13 @@ metadatas = emb_data["metadatas"]
 # Mesmo modelo de embeddings usado no build_index.py
 embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
-# Histórico do chat: lista de (user, assistant)
-dialog_history = []
+# ✅ Gradio atual espera "messages format":
+# lista de dicts: {"role": "user"/"assistant", "content": "..."}
+dialog_history: List[Dict[str, str]] = []
 
 
 # ========= Recuperação de contexto =========
 def retrieve_context(query: str, k: int = 4) -> str:
-    """Busca k trechos mais relevantes no índice FAISS para a pergunta."""
     if not query or not query.strip():
         return ""
 
@@ -113,8 +112,7 @@ def retrieve_context(query: str, k: int = 4) -> str:
 
 
 # ========= Streaming da NVIDIA =========
-def nv_stream(messages, temperature: float, top_p: float, max_tokens: int):
-    """Stream da resposta da NVIDIA (LLaMA 3)."""
+def nv_stream(messages: List[Dict[str, str]], temperature: float, top_p: float, max_tokens: int):
     reply = ""
     stream = client.chat.completions.create(
         model=CHAT_MODEL,
@@ -154,20 +152,25 @@ def chatbot(user_input: str, temperature: float, top_p: float, max_tokens: int):
         ),
     }
 
-    messages = [system_msg]
-    for u, a in dialog_history:
-        messages.append({"role": "user", "content": u})
-        messages.append({"role": "assistant", "content": a})
-    messages.append({"role": "user", "content": user_input})
+    # Mensagens que vão para o modelo = system + histórico + user atual
+    messages: List[Dict[str, str]] = [system_msg] + dialog_history + [{"role": "user", "content": user_input}]
 
     reply_full = ""
     try:
         for partial in nv_stream(messages, temperature, top_p, max_tokens):
             reply_full = partial
-        dialog_history.append((user_input, reply_full))
+
+        # Atualiza histórico no formato messages (compatível com Gradio)
+        dialog_history = dialog_history + [
+            {"role": "user", "content": user_input},
+            {"role": "assistant", "content": reply_full},
+        ]
     except Exception as e:
         reply_full = f"⚠️ Erro na API NVIDIA: {type(e).__name__}: {e}"
-        dialog_history.append((user_input, reply_full))
+        dialog_history = dialog_history + [
+            {"role": "user", "content": user_input},
+            {"role": "assistant", "content": reply_full},
+        ]
 
     return dialog_history, ""
 
@@ -178,7 +181,7 @@ def clear_history():
     return [], ""
 
 
-# ========= CSS simples / layout padrão =========
+# ========= CSS =========
 custom_css = r"""
 body, .gradio-container {
     background: #ffffff;
@@ -258,9 +261,7 @@ body, .gradio-container {
 
 
 # ========= Layout Gradio =========
-# Gradio 6+: css e theme foram movidos para o launch()
 with gr.Blocks(title=APP_TITLE) as demo:
-    # Header
     with gr.Group(elem_id="header-box"):
         gr.HTML(
             f"""
@@ -276,12 +277,11 @@ with gr.Blocks(title=APP_TITLE) as demo:
     gr.Markdown(INTRO)
 
     with gr.Row():
-        # Coluna principal (chat)
         with gr.Column(scale=3):
             with gr.Group(elem_classes="card"):
                 gr.Markdown("### 💬 Conversa Jurídica")
 
-                # ✅ REMOVIDO: type="tuples" (não existe nesta versão)
+                # ✅ Agora o valor/retorno é messages-format (dicts role/content)
                 chatbot_ui = gr.Chatbot(
                     elem_id="chat-window",
                     label="Chatbot",
@@ -302,30 +302,15 @@ with gr.Blocks(title=APP_TITLE) as demo:
                     top_p = gr.Slider(0, 1, value=0.9, label="Top-p")
                     max_tokens = gr.Slider(64, 2048, value=512, step=64, label="Max Tokens")
 
-            btn_send.click(
-                chatbot,
-                [txt, temperature, top_p, max_tokens],
-                [chatbot_ui, txt],
-            )
-            txt.submit(
-                chatbot,
-                [txt, temperature, top_p, max_tokens],
-                [chatbot_ui, txt],
-            )
-            btn_clear.click(
-                clear_history,
-                [],
-                [chatbot_ui, txt],
-            )
-
-        # Sidebar
+            btn_send.click(chatbot, [txt, temperature, top_p, max_tokens], [chatbot_ui, txt])
+            txt.submit(chatbot, [txt, temperature, top_p, max_tokens], [chatbot_ui, txt])
+            btn_clear.click(clear_history, [], [chatbot_ui, txt])
+
         with gr.Column(scale=2):
             with gr.Group(elem_classes="card"):
                 gr.Markdown("### 💡 Sugestões rápidas")
                 for q in SUGGESTION_QUESTIONS:
-                    gr.Button(q, elem_classes="suggestion-btn").click(
-                        lambda s=q: s, outputs=[txt]
-                    )
+                    gr.Button(q, elem_classes="suggestion-btn").click(lambda s=q: s, outputs=[txt])
 
                 gr.Markdown("---")
                 gr.Markdown("### 📚 Explorar por tema")
@@ -333,16 +318,13 @@ with gr.Blocks(title=APP_TITLE) as demo:
                 for theme, qs in SUGGESTIONS_THEMES.items():
                     with gr.Accordion(theme, open=False):
                         for q in qs:
-                            gr.Button(q, elem_classes="suggestion-btn").click(
-                                lambda s=q: s, outputs=[txt]
-                            )
+                            gr.Button(q, elem_classes="suggestion-btn").click(lambda s=q: s, outputs=[txt])
+
+    gr.Markdown('<div class="app-footer">EcoLexIA · Sistema RAG para legislação ambiental em Portugal</div>')
 
-    gr.Markdown(
-        '<div class="app-footer">EcoLexIA · Sistema RAG para legislação ambiental em Portugal</div>'
-    )
 
-# Para Hugging Face Spaces basta que a variável `demo` exista;
-# manter o launch permite rodar localmente.
 if __name__ == "__main__":
+    # Gradio 6+: css e theme no launch
     demo.launch(theme=gr.themes.Soft(), css=custom_css)
 
+