Update app.py

app.py

@@ -1,12 +1,7 @@
 import os, io, tempfile, warnings
 import numpy as np
 import gradio as gr
-import os
 
-
-
-os.environ["OMP_NUM_THREADS"] = "4"
-os.environ["MKL_NUM_THREADS"] = "4"
 # =========================
 # TensorFlow / Keras
 # =========================
@@ -206,15 +201,15 @@ def to_verdict(score):
 def run_inference(video_file):
     lazy_load()
     if video_file is None:
-        return 0.0, "No video provided", "Please upload a video file."
+        return None, None, "Please upload a video file."
 
     video_prob, vmsg = predict_video_prob(video_file)
     if video_prob is None:
-        return 0.0, "No Prediction", vmsg or "Unable to process the video."
+        return None, None, vmsg or "Unable to process the video."
 
     verdict = f"VIDEO ONLY: {to_verdict(video_prob)}"
-    return round(float(video_prob), 4), verdict, ""
-
+    fmt = lambda x: None if x is None else round(float(x), 4)
+    return fmt(video_prob), verdict, None
 
 # =========================
 # Gradio UI
@@ -238,13 +233,5 @@ with gr.Blocks(title="Deepfake Detector — Video Only (GPU-ready)") as demo:
     go.click(run_inference, inputs=[video_in], outputs=[v_out, verdict_out, msg_out])
 
 if __name__ == "__main__":
-    import os
-    os.environ["OMP_NUM_THREADS"] = "4"
-    os.environ["MKL_NUM_THREADS"] = "4"
-
-    print("[INFO] Video model loaded.")
-    print("[INFO] MTCNN ready on cuda.")
-    print("[INFO] dlib detector + predictor ready.")
-
+    lazy_load()
     demo.launch(server_name="0.0.0.0", server_port=7860)
-