Update app.py

app.py

@@ -1,69 +1,62 @@
 import gradio as gr
 import joblib
-from huggingface_hub import hf_hub_download
 import os
 
 
-
 def load_model():
-    """Download and load model from HF Hub"""
+    """Load model from local files in the Space"""
     try:
-        print("Loading model from Hugging Face Hub...")
+        print("Loading model from local files...")
         
-        # Your HF settings
-        HF_USERNAME = "Daizzyy"
-        HF_MODEL = "cyberbullying-model"
-        repo_id = f"{HF_USERNAME}/{HF_MODEL}"
+        print("\nFiles in current directory:")
+        for f in os.listdir("."):
+            if f.endswith((".jobilib", ".pkl", ".txt")):
+                print(f"  - {f}")
         
-        print(f"Downloading from: {repo_id}")
+       
+        model_files = [
+            "tfidf_logreg_best.jobilib",
+            "model.jobilib",
+            "model.pkl"
+        ]
         
-        # 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",
-                force_download=True,
-                resume_download=True
-            )
-            model = joblib.load(model_file)
-            print(f"✅ Model loaded successfully")
-        except Exception as e:
-            print(f"❌ Error loading model: {e}")
+        model = None
+        for mf in model_files:
+            if os.path.exists(mf):
+                print(f"\nLoading model: {mf}")
+                model = joblib.load(mf)
+                print(f"✅ Model loaded from {mf}")
+                break
+        
+        if model is None:
+            print("❌ No model file found!")
             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",
-                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}")
-            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",
-                force_download=True,
-                resume_download=True
-            )
-            label_encoder = joblib.load(label_encoder_file)
-            print(f"✅ Label encoder loaded successfully")
-        except Exception as e:
-            print(f"⚠️ Label encoder not found (optional): {e}")
-            label_encoder = None
+        vectorizer_files = [
+            "vocab.txt",
+            "vocab",
+            "vectorizer.jobilib",
+            "tfidf.jobilib"
+        ]
+        
+        vectorizer = None
+        for vf in vectorizer_files:
+            if os.path.exists(vf):
+                print(f"Loading vectorizer: {vf}")
+                vectorizer = joblib.load(vf)
+                print(f"✅ Vectorizer loaded from {vf}")
+                break
+        
+        if vectorizer is None:
+            print("⚠️ Vectorizer not found")
+        
+        # Try to load label encoder
+        label_encoder = None
+        if os.path.exists("label_encoder.jobilib"):
+            print("Loading label encoder...")
+            label_encoder = joblib.load("label_encoder.jobilib")
+            print("✅ Label encoder loaded")
         
         return {
             "model": model,
@@ -72,7 +65,7 @@ def load_model():
         }
         
     except Exception as e:
-        print(f"❌ Critical error: {str(e)}")
+        print(f"❌ Error loading model: {str(e)}")
         import traceback
         print(traceback.format_exc())
         return None
@@ -96,7 +89,7 @@ def predict(text):
     try:
         # Check if models are loaded
         if model_components is None:
-            return "<div class='warn'>❌ Model not loaded. Please check server logs.</div>"
+            return "<div class='warn'>❌ Model not loaded. Check logs.</div>"
         
         model = model_components["model"]
         vectorizer = model_components["vectorizer"]