Update app.py

app.py

@@ -173,36 +173,13 @@ def create_brand_categories():
         }
     }
 
-@st.cache_resource
-def download_file_from_google_drive(file_id):
-    """Downloads a file from Google Drive using gdown."""
-    url = f"https://drive.google.com/uc?id={file_id}"
-    try:
-        with st.spinner('Downloading from Google Drive...'):
-            output = f"temp_{file_id}.pkl"
-            gdown.download(url, output, quiet=False)
-            
-            with open(output, 'rb') as f:
-                content = f.read()
-            
-            # Clean up the temporary file
-            os.remove(output)
-            return content
-            
-    except Exception as e:
-        st.error(f"Error downloading from Google Drive: {str(e)}")
-        raise e
-
 @st.cache_data
 def load_datasets():
-    """Load the dataset from Google Drive."""
-    dataset_file_id = "1emG-BQ3-x4xsMAGMEznkh1ACdlAj5Dn1"
-    
     try:
         with st.spinner('Loading dataset...'):
-            content = download_file_from_google_drive(dataset_file_id)
             # Use BytesIO to read the CSV content
-            original_data = pd.read_csv(BytesIO(content), low_memory=False)
+            original_data = pd.read_csv(BytesIO(CTP_Model1.csv), low_memory=False)
+            
             
             # Ensure column names match the model's expectations
             original_data.columns = original_data.columns.str.strip().str.capitalize()
@@ -213,18 +190,11 @@ def load_datasets():
 
 @st.cache_resource
 def load_model_and_encodings():
-    """Load model from Google Drive and create encodings."""
-    model_file_id = "1wKixkdW2pVKEpJW-N1QIyKUr2nYirU7I"
-    
     try:
         # Show loading message
         with st.spinner('Loading model...'):
-            try:
-                model_content = hf_hub_download(repo_id="EdBoy2202/car_prediction_model", filename="car_price_modelv3.pkl")
-                model = joblib.load(BytesIO(model_content))
-            except:
-                model_content = download_file_from_google_drive(model_file_id)
-                model = joblib.load(BytesIO(model_content))
+            model_content = hf_hub_download(repo_id="EdBoy2202/car_prediction_model", filename="car_price_modelv3.pkl")
+            model = joblib.load(BytesIO(model_content))
         
         # Load data for encodings
         original_data = load_datasets()