Update app.py

app.py

@@ -13,7 +13,7 @@ NV_API_KEY = os.environ.get("NV_API_KEY")
 if not NV_API_KEY:
     raise RuntimeError(
         "🔒 NV_API_KEY not set. "
-        "In your Hugging Face Space, go to Settings → Variables & Secrets and create NV_API_KEY."
+        "Configure it in Settings → Variables & Secrets."
     )
 
 # NVIDIA-compatible OpenAI client for chat
@@ -29,7 +29,7 @@ CHAT_MODEL = "meta/llama3-8b-instruct"
 APP_TITLE = "CVchat – Ronaldo Menezes"
 INTRO = (
     "👋 Olá! Eu sou o CVchat do Ronaldo Menezes.\n"
-    "Converse sobre minha experiência, projetos, tecnologias, resultados e muito mais.\n\n"
+    "Converse sobre minha experiência, projetos, tecnologias e resultados.\n\n"
     "Exemplos de perguntas:\n"
     "• Quem é o Ronaldo Menezes\n"
     "• Resuma sua experiência com Process Mining.\n"
@@ -45,23 +45,23 @@ SUGGESTION_QUESTIONS = [
     "Certificações?",
 ]
 
-# Paths for FAISS files
+# Paths for FAISS index and chunks
 INDEX_FILE  = "r_docs.index"
 CHUNKS_FILE = "r_chunks.npy"
 PDF_PATH    = "CV-Ronaldo_Menezes_2025_06.pdf"
 
-# verify index files exist
+# Verify that the index files exist
 if not Path(INDEX_FILE).exists() or not Path(CHUNKS_FILE).exists():
     raise FileNotFoundError(
         "Index not found. Run build_index.py to generate r_docs.index and r_chunks.npy."
     )
 
-# load FAISS index and chunks
+# Load FAISS index and chunks
 index  = faiss.read_index(INDEX_FILE)
 chunks = np.load(CHUNKS_FILE, allow_pickle=True)
 
 # ----------------------------
-# Local embedding model (context retrieval)
+# Context retrieval (local embeddings)
 # ----------------------------
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 
@@ -83,7 +83,7 @@ def chatbot(user_input: str, temperature: float, top_p: float, max_tokens: int):
     if not user_input:
         return dialog_history, ""
 
-    # build system message with retrieved context
+    # Retrieve context
     context = retrieve_context(user_input)
     system_msg = {
         "role": "system",
@@ -94,16 +94,16 @@ def chatbot(user_input: str, temperature: float, top_p: float, max_tokens: int):
         )
     }
 
-    # assemble conversation
+    # Build messages list
     messages = [system_msg]
     for u, a in dialog_history:
-        messages += [
+        messages.extend([
             {"role": "user", "content": u},
             {"role": "assistant", "content": a}
-        ]
+        ])
     messages.append({"role": "user", "content": user_input})
 
-    # call NVIDIA chat API
+    # Call NVIDIA chat API in streaming mode
     assistant_reply = ""
     try:
         stream = client.chat.completions.create(
@@ -121,11 +121,12 @@ def chatbot(user_input: str, temperature: float, top_p: float, max_tokens: int):
     except OpenAIError as e:
         assistant_reply = f"⚠️ API Error: {e.__class__.__name__}: {e}"
 
+    # Update history and return
     dialog_history.append((user_input, assistant_reply))
     return dialog_history, ""
 
 # ----------------------------
-# Clear chat history
+# Clear history
 # ----------------------------
 def clear_history():
     global dialog_history
@@ -144,44 +145,13 @@ custom_css = r"""
   --radius: 8px;
   --spacing: 1rem;
 }
-body {
-  background: var(--bg-light);
-  color: var(--txt-dark);
-  font-family: 'Helvetica Neue', sans-serif;
-}
-#chat-window {
-  height: 65vh;
-  overflow-y: auto;
-  padding: var(--spacing);
-  border: 1px solid #ddd;
-  border-radius: var(--radius);
-}
-#input-area {
-  display: flex;
-  margin-top: var(--spacing);
-}
-#user-input {
-  flex: 1;
-  padding: 0.6rem;
-  border: 1px solid #ccc;
-  border-radius: var(--radius) 0 0 var(--radius);
-}
-#send-button {
-  padding: 0 1rem;
-  background: var(--primary);
-  color: white;
-  border: none;
-  border-radius: 0 var(--radius) var(--radius) 0;
-  cursor: pointer;
-}
-.sidebar {
-  background: var(--bg-light);
-  padding: var(--spacing);
-  border-left: 1px solid #eee;
-}
-.sidebar h3 {
-  margin-top: 0;
-}
+body { background: var(--bg-light); color: var(--txt-dark); font-family: 'Helvetica Neue', sans-serif; }
+#chat-window { height: 65vh; overflow-y: auto; padding: var(--spacing); border: 1px solid #ddd; border-radius: var(--radius); }
+#input-area { display: flex; margin-top: var(--spacing); }
+#user-input { flex: 1; padding: 0.6rem; border: 1px solid #ccc; border-radius: var(--radius) 0 0 var(--radius); }
+#send-button { padding: 0 1rem; background: var(--primary); color: white; border: none; border-radius: 0 var(--radius) var(--radius) 0; cursor: pointer; }
+.sidebar { background: var(--bg-light); padding: var(--spacing); border-left: 1px solid #eee; }
+.sidebar h3 { margin-top: 0; }
 """
 
 with gr.Blocks(title=APP_TITLE, css=custom_css, theme=gr.themes.Base()) as demo:
@@ -189,21 +159,22 @@ with gr.Blocks(title=APP_TITLE, css=custom_css, theme=gr.themes.Base()) as demo:
     gr.Markdown(INTRO)
 
     with gr.Row():
-        # main chat column
+        # Main chat column
         with gr.Column(scale=3):
-            chatbot_ui = gr.Chatbot(type="tuples", elem_id="chat-window")
-            with gr.Row(elem_id="input-area"):
-                txt = gr.Textbox(placeholder="Digite sua pergunta…", lines=2, elem_id="user-input")
-                btn = gr.Button("Enviar", elem_id="send-button")
-            btn.click(chatbot,
-                      [txt, gr.Slider(0,1,0.6), gr.Slider(0,1,0.95), gr.Slider(64,2048,512)],
-                      [chatbot_ui, txt])
-            txt.submit(chatbot,
-                       [txt, gr.Slider(0,1,0.6), gr.Slider(0,1,0.95), gr.Slider(64,2048,512)],
-                       [chatbot_ui, txt])
+            chatbot_ui   = gr.Chatbot(type="tuples", elem_id="chat-window")
+            txt          = gr.Textbox(placeholder="Digite sua pergunta…", lines=2, elem_id="user-input")
+            btn          = gr.Button("Enviar", elem_id="send-button")
+            # Advanced settings sliders
+            temperature = gr.Slider(0, 1, value=0.6, label="Temperature")
+            top_p       = gr.Slider(0, 1, value=0.95, label="Top-p")
+            max_tokens  = gr.Slider(64, 2048, value=512, step=64, label="Max Tokens")
+
+            # Bind events
+            btn.click(chatbot, [txt, temperature, top_p, max_tokens], [chatbot_ui, txt])
+            txt.submit(chatbot, [txt, temperature, top_p, max_tokens], [chatbot_ui, txt])
             gr.Button("Limpar").click(clear_history, [], [chatbot_ui, txt])
 
-        # sidebar with PDF & suggestions
+        # Sidebar with PDF & suggestions
         with gr.Column(scale=1, elem_classes="sidebar"):
             if Path(PDF_PATH).exists():
                 gr.Markdown(f"[📄 Baixar CV em PDF](/file={PDF_PATH})")