Update app.py

app.py

@@ -1,13 +1,28 @@
 import gradio as gr
 import pickle
-import sklearn  # Ensure scikit-learn is available
+import os
+import sys
 
-# Load the trained model and vectorizer safely
+# Add debugging information
+print("Current directory:", os.getcwd())
+print("Files in directory:", os.listdir())
+
+# Load the trained model and vectorizer with better error handling
 try:
-    model = pickle.load(open('model.pkl', 'rb'))  # Ensure correct file name
-    vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))
+    model_path = 'model.pkl'
+    vectorizer_path = 'vectorizer.pkl'
+    
+    print(f"Loading model from {model_path}")
+    model = pickle.load(open(model_path, 'rb'))
+    
+    print(f"Loading vectorizer from {vectorizer_path}")
+    vectorizer = pickle.load(open(vectorizer_path, 'rb'))
+    
+    print("Model and vectorizer loaded successfully")
 except Exception as e:
-    print(f"Error loading model: {e}")
+    print(f"Error loading model or vectorizer: {e}")
+    print(f"Python version: {sys.version}")
+    print(f"System path: {sys.path}")
 
 def predict_sms(message):
     try:
@@ -15,7 +30,9 @@ def predict_sms(message):
         prediction = model.predict(transformed_text)[0]
         return "Spam" if prediction == 1 else "Not Spam"
     except Exception as e:
-        return f"Error: {e}"
+        error_msg = f"Error during prediction: {e}"
+        print(error_msg)
+        return error_msg
 
 # Gradio Web Interface
 iface = gr.Interface(
@@ -26,5 +43,5 @@ iface = gr.Interface(
     description="Enter a message to check if it's spam or not."
 )
 
-# Ensure Hugging Face properly serves the app
-iface.launch(server_name="0.0.0.0", server_port=7860)
+# For Hugging Face deployment
+iface.launch(server_name="0.0.0.0", server_port=7860)