Update model.py

model.py

@@ -15,15 +15,13 @@ import numpy as np
 # === Load Hugging Face Pipelines ===
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
-emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
+emotion_pipeline = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", top_k=1)
 
 # === Summarization Functions ===
 def summarize_review(text, max_len=60, min_len=10):
-    """Transformer-based summarization (brief)"""
     return summarizer(text, max_length=max_len, min_length=min_len, do_sample=False)[0]["summary_text"]
 
 def smart_summarize(text, n_clusters=1):
-    """TF-IDF Clustering based summarization"""
     sentences = sent_tokenize(text)
     if len(sentences) <= 1:
         return text
@@ -54,7 +52,7 @@ def detect_emotion(text):
         result = emotion_pipeline(text)[0]
         return result["label"]
     except Exception:
-        return "unknown"
+        return "neutral"
 
 # === Follow-up Q&A (single or multi) ===
 def answer_followup(text, question, verbosity="brief"):