Update app.py

app.py

@@ -1,7 +1,6 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
 import torch
 import gradio as gr
-from sentence_transformers import SentenceTransformer, util  # for similarity gating
 
 model_id = "eduard76/Llama3-8b-good-new"
 
@@ -23,7 +22,18 @@ model = AutoModelForCausalLM.from_pretrained(
 
 pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
+# 🔐 Lista de topicuri din dataset (poți ajusta manual dacă vrei):
+covered_topics = {
+    "ospf", "bgp", "vxlan", "evpn", "network design", "acl", "routing",
+    "spine", "leaf", "underlay", "overlay", "mpls", "qos", "firewall",
+    "vpn", "vlan", "subnet", "cidr"
+}
+
 def chat(user_input):
+    # Verificăm dacă întrebarea conține topicuri cunoscute
+    if not any(topic in user_input.lower() for topic in covered_topics):
+        return "Îmi pare rău, nu am suficiente date despre acest subiect pentru a răspunde corect."
+
     prompt = f"User: {user_input}\nAI:"
     response = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.7)[0]["generated_text"]
     return response[len(prompt):].strip()