Update app.py

app.py

@@ -4,18 +4,17 @@ import numpy as np
 import torch
 from transformers import AutoImageProcessor, SiglipForImageClassification
 from PIL import Image
-import os
 
-# Load model and processor
+# ✅ Load model and processor
 model_name = "prithivMLmods/deepfake-detector-model-v1"
 processor = AutoImageProcessor.from_pretrained(model_name)
 model = SiglipForImageClassification.from_pretrained(model_name)
 model.eval()
 
-# Haar face detector
+# ✅ Haar cascade face detector
 face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
-# Inference function
+# ✅ Inference logic
 def detect_deepfake(video):
     if video is None:
         return "❌ Please upload a valid MP4 video."
@@ -41,7 +40,7 @@ def detect_deepfake(video):
         faces = face_detector.detectMultiScale(gray, 1.1, 4)
 
         if len(faces) > 0:
-            x, y, w, h = faces[0]  # Use only first detected face
+            x, y, w, h = faces[0]  # Only the first face for speed
             face = frame[y:y+h, x:x+w]
             if face.size == 0:
                 continue
@@ -55,6 +54,7 @@ def detect_deepfake(video):
                 logits = model(**inputs).logits
                 prob = torch.softmax(logits, dim=-1)[0][1].item()
                 frame_preds.append(prob)
+
                 if prob > 0.6:
                     fake_count += 1
                 else:
@@ -71,23 +71,23 @@ def detect_deepfake(video):
     verdict = "FAKE" if fake_count > real_count else "REAL"
 
     return f"""
-    ✅ **Result: {verdict}**
+    ✅ **Final Result: {verdict}**
     🟢 Real Frames: {real_count}
     🔴 Fake Frames: {fake_count}
     📊 Avg Confidence: {avg_conf:.2f}
     """
 
-# Gradio app using Blocks (more stable)
+# ✅ Gradio app using Blocks (queue-safe)
 with gr.Blocks() as demo:
     gr.Markdown("## 🎭 Fast Deepfake Video Detector")
-    gr.Markdown("Upload a short .mp4 video (under 50MB). Model will analyze and classify it as REAL or FAKE based on face analysis.")
-    
+    gr.Markdown("Upload a short `.mp4` video (under 50MB). The model analyzes faces and detects if the video is REAL or FAKE.")
+
     with gr.Row():
         video_input = gr.Video(label="📤 Upload your video")
-        result_output = gr.Markdown(label="🧠 Deepfake Detection Result")
+        result_output = gr.Markdown(label="🧠 Detection Result")
 
-    analyze_btn = gr.Button("Analyze Video")
+    analyze_btn = gr.Button("🔍 Analyze Video")
 
     analyze_btn.click(fn=detect_deepfake, inputs=video_input, outputs=result_output)
 
-demo.queue(concurrency_count=1).launch()
+demo.queue().launch()