Update app.py

app.py

@@ -4,17 +4,10 @@ from sentence_transformers import SentenceTransformer
 import numpy as np
 from typing import Dict, List, Tuple
 from textblob import TextBlob
-import nltk
-from nltk.tokenize import sent_tokenize
-
-# Download NLTK data
-nltk.download(['punkt', 'punkt_tab'])
-nltk.download('punkt', language='portuguese')
 
 # Load embeddings model
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
-# Define questions with categories and context
 PERGUNTAS = [
     {
         "categoria": "autoconhecimento",
@@ -43,7 +36,6 @@ PERGUNTAS = [
     }
 ]
 
-# Add tone categories
 TONE_PATTERNS = {
     "confiante": ["certamente", "sem dúvida", "confio", "tenho certeza", "definitivamente"],
     "reflexivo": ["penso que", "considero", "reflito", "analiso", "pondero"],
@@ -52,7 +44,6 @@ TONE_PATTERNS = {
     "emocional": ["sinto", "emocionalmente", "impactado", "motivado", "frustrado"]
 }
 
-# Enhanced response system
 RESPOSTAS_COACH = {
     "autoconhecimento": {
         "positive": [
@@ -140,10 +131,7 @@ class EnhancedCoach:
         self.response_quality_metrics = []
         
     def analyze_response_quality(self, text: str) -> Dict[str, float]:
-        try:
-            sentences = sent_tokenize(text, language='portuguese')
-        except LookupError:
-            sentences = [s.strip() for s in text.split('.') if s.strip()]
+        sentences = [s.strip() for s in text.split('.') if s.strip()]
         
         metrics = {
             "depth": self._calculate_depth(text),