Upload app.py with huggingface_hub

app.py

@@ -3,17 +3,14 @@ import torch
 import gradio as gr
 from torchvision import transforms
 from model import HybridDeepfakeDetector
-from huggingface_hub import hf_hub_download
 
 model = HybridDeepfakeDetector()
-weights_path = hf_hub_download(
-    repo_id="AdityaManojShinde/deepfake-detector",
-    filename="deepfake_detector_phase2.pth"
-)
 model.load_state_dict(
-    torch.load(weights_path, map_location="cpu", weights_only=True)
+    torch.load("deepfake_detector_phase2.pth", 
+               map_location="cpu", 
+               weights_only=True)
 )
-model.eval()  # disables dropout permanently
+model.eval()
 
 transform = transforms.Compose([
     transforms.Resize((224, 224)),
@@ -28,7 +25,7 @@ def predict(image):
     tensor = transform(image).unsqueeze(0)
     with torch.no_grad():
         prob = model(tensor).item()
-    print(f"Raw prob: {prob}")  # debug line
+    print(f"Raw prob: {prob:.4f}")
     label = "REAL" if prob > 0.5 else "FAKE"
     confidence = prob if label == "REAL" else 1 - prob
     return f"{label} ({confidence*100:.1f}% confident)"