Update app.py

app.py

@@ -4,25 +4,28 @@ from huggingface_hub import hf_hub_download
 import os
 
 
+
 def load_model():
     """Download and load model from HF Hub"""
     try:
         print("Loading model from Hugging Face Hub...")
         
-       
-      
-        HF_USERNAME = "Daizzyy" 
-        HF_MODEL = "cyberbullying-model"  
+        # Your HF settings
+        HF_USERNAME = "Daizzyy"
+        HF_MODEL = "cyberbullying-model"
         repo_id = f"{HF_USERNAME}/{HF_MODEL}"
         
         print(f"Downloading from: {repo_id}")
         
-        
+        # Download model file
+        print("Downloading: tfidf_logreg_best.jobilib")
         try:
             model_file = hf_hub_download(
                 repo_id=repo_id,
                 filename="tfidf_logreg_best.jobilib",
-                cache_dir=".cache"
+                cache_dir=".cache",
+                force_download=True,
+                resume_download=True
             )
             model = joblib.load(model_file)
             print(f"✅ Model loaded successfully")
@@ -30,23 +33,31 @@ def load_model():
             print(f"❌ Error loading model: {e}")
             return None
         
+        # Download vectorizer (vocab.txt)
+        print("Downloading: vocab.txt")
         try:
             vectorizer_file = hf_hub_download(
                 repo_id=repo_id,
                 filename="vocab.txt",
-                cache_dir=".cache"
+                cache_dir=".cache",
+                force_download=True,
+                resume_download=True
             )
             vectorizer = joblib.load(vectorizer_file)
             print(f"✅ Vectorizer loaded successfully")
         except Exception as e:
-            print(f"❌ Error loading vectorizer: {e}")
+            print(f"⚠️ Error loading vectorizer: {e}")
             vectorizer = None
         
+        # Download label encoder (optional)
+        print("Downloading: label_encoder.jobilib")
         try:
             label_encoder_file = hf_hub_download(
                 repo_id=repo_id,
                 filename="label_encoder.jobilib",
-                cache_dir=".cache"
+                cache_dir=".cache",
+                force_download=True,
+                resume_download=True
             )
             label_encoder = joblib.load(label_encoder_file)
             print(f"✅ Label encoder loaded successfully")
@@ -61,7 +72,7 @@ def load_model():
         }
         
     except Exception as e:
-        print(f"❌ Critical error loading model: {str(e)}")
+        print(f"❌ Critical error: {str(e)}")
         import traceback
         print(traceback.format_exc())
         return None
@@ -119,7 +130,7 @@ def predict(text):
         
         print(f"Prediction: {label}, Score: {score:.4f}")
         
-        # Category definitions with colors and emojis
+        # Category definitions
         cyberbullying_types = {
             "age": {"emoji": "👶", "color": "#ff6b6b", "text": "Age-Based Cyberbullying"},
             "gender": {"emoji": "⚥️", "color": "#ff8c42", "text": "Gender-Based Cyberbullying"},
@@ -129,7 +140,7 @@ def predict(text):
             "not_cyberbullying": {"emoji": "✅", "color": "#00ff64", "text": "Safe Message"}
         }
         
-        # Get category info (handle case variations)
+        # Get category info
         label_lower = str(label).lower().strip()
         category = cyberbullying_types.get(
             label_lower, 
@@ -171,7 +182,7 @@ def predict(text):
 
 
 
-with gr.Blocks(theme=gr.themes.Soft(), css="""
+with gr.Blocks(css="""
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/animate.css/4.1.1/animate.min.css"/>
 
 * {