Update app.py

app.py

@@ -1,15 +1,36 @@
 import gradio as gr
 from transformers import pipeline
 import os
+import neattext.functions as nfx
+import re
 
 model = pipeline("text-classification", model="i0xs0/Emotion_Detection", tokenizer="i0xs0/Emotion_Detection")
 
+def clean_text(text):
 
+    if not isinstance(text, str):
+            return text
+
+    text = nfx.remove_userhandles(text)        # Remove user handles (@username)
+    text = nfx.remove_punctuations(text)       # Remove punctuation marks (!, ?, .)
+    text = nfx.remove_accents(text)            # Remove accents from characters (e.g., é -> e)
+    text = nfx.remove_urls(text)               # Remove URLs (e.g., https://example.com)
+    text = nfx.remove_emojis(text)             # Remove emojis (e.g., 😊, 🚀)
+    text = nfx.remove_emails(text)             # Remove email addresses (e.g., user@example.com)
+    text = nfx.remove_phone_numbers(text)      # Remove phone numbers (e.g., +1234567890)
+    text = nfx.remove_html_tags(text)          # Remove HTML tags (<div>, <p>)
+    text=re.sub(r"[^a-zA-Z0-9\s']", "", text)  # Remove special characters
+    text = nfx.remove_multiple_spaces(text)    # Remove multiple spaces and reduce them to a single space
+    text = nfx.remove_md5sha(text)             # Remove MD5 or SHA-like hash strings
+
+    return text
     
 def predict_emotion(text):
 
+    cleaned_text = clean_text(text)  
+    print("Processed Text:", cleaned_text)  
 
-    results = model(text)  
+    results = model(cleaned_text)  
     return {item["label"]: item["score"] for item in results}