Load model from local files instead of HF Hub

app.py

@@ -32,9 +32,8 @@ import gradio as gr
 
 # ========== CONFIGURATION ==========
 
-# Hugging Face model ID and token
-HF_MODEL_ID = os.getenv("HF_MODEL_ID", "Meet2304/convnextv2-cervical-cell-classification")
-HF_TOKEN = os.getenv("HF_TOKEN")
+# Model path (local files in the Space)
+MODEL_PATH = os.path.dirname(os.path.abspath(__file__))
 
 # Class names
 CLASS_NAMES = [
@@ -59,16 +58,16 @@ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # ========== MODEL LOADING ==========
 
-print("Loading model from Hugging Face...")
-print(f"Model ID: {HF_MODEL_ID}")
+print("Loading model from local files...")
+print(f"Model path: {MODEL_PATH}")
 print(f"Device: {DEVICE}")
 
-# Load image processor
-processor = AutoImageProcessor.from_pretrained(HF_MODEL_ID, token=HF_TOKEN)
+# Load image processor from local files
+processor = AutoImageProcessor.from_pretrained(MODEL_PATH)
 print("✓ Processor loaded")
 
-# Load model
-model = ConvNextV2ForImageClassification.from_pretrained(HF_MODEL_ID, token=HF_TOKEN)
+# Load model from local files
+model = ConvNextV2ForImageClassification.from_pretrained(MODEL_PATH)
 model = model.to(DEVICE)
 model.eval()
 print("✓ Model loaded and set to evaluation mode")