Update app.py

app.py

@@ -5,29 +5,32 @@ import torch
 from transformers import AutoImageProcessor, SiglipForImageClassification
 from PIL import Image
 import matplotlib.pyplot as plt
-import tempfile
-import os
 
-# ✅ Load model once
+# 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()
 
-# ✅ Load OpenCV Haar face detector
+# Face detector
 face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
 
 def analyze_deepfake(video_path):
     cap = cv2.VideoCapture(video_path)
     frame_preds = []
     frame_count = 0
-    max_frames = 60
+    max_frames = 30   # ✅ Reduced for speed
+    frame_skip = 5    # ✅ Process every 5th frame
 
     while True:
         ret, frame = cap.read()
         if not ret or frame_count >= max_frames:
             break
 
+        if frame_count % frame_skip != 0:
+            frame_count += 1
+            continue
+
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
         faces = face_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
 
@@ -38,7 +41,8 @@ def analyze_deepfake(video_path):
                 continue
 
             face_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
-            inputs = processor(images=Image.fromarray(face_rgb), return_tensors="pt")
+            pil_img = Image.fromarray(face_rgb)
+            inputs = processor(images=pil_img, return_tensors="pt")
 
             with torch.no_grad():
                 logits = model(**inputs).logits
@@ -55,36 +59,33 @@ def analyze_deepfake(video_path):
 
     cap.release()
 
-    # Final Result
     if frame_preds:
         avg = np.mean(frame_preds)
         verdict = "FAKE" if avg > 0.5 else "REAL"
         result_text = f"✅ FINAL RESULT: **{verdict}** (confidence: {avg:.2f})"
     else:
-        result_text = "❌ No faces detected. Please try another video."
+        result_text = "❌ No faces detected. Try a clearer video."
 
-    # Plot histogram
+    # Generate graph directly
     fig, ax = plt.subplots(figsize=(6, 4))
     ax.hist(frame_preds, bins=10, color="orange", edgecolor="black")
     ax.set_title("Fake Confidence per Frame")
-    ax.set_xlabel("Confidence (0=Real, 1=Fake)")
+    ax.set_xlabel("Confidence (0 = Real, 1 = Fake)")
     ax.set_ylabel("Frame Count")
     ax.grid(True)
 
-    # Save plot to temp file
-    plot_path = os.path.join(tempfile.gettempdir(), "plot.png")
-    plt.savefig(plot_path)
-    plt.close(fig)
-
-    return result_text, plot_path
+    return result_text, fig
 
-# ✅ Gradio Interface
+# Gradio UI
 demo = gr.Interface(
     fn=analyze_deepfake,
-    inputs=gr.Video(label="📤 Upload a video (MP4 only)"),
-    outputs=[gr.Markdown(label="📊 Result"), gr.Image(type="filepath", label="📈 Confidence Histogram")],
-    title="🎭 Deepfake Video Detection App",
-    description="Upload a video. The model will detect faces and determine if it's REAL or FAKE using frame-level deepfake classification.",
+    inputs=gr.Video(label="📤 Upload MP4 video"),
+    outputs=[
+        gr.Markdown(label="🧠 Analysis Result"),
+        gr.Plot(label="📈 Confidence Histogram")
+    ],
+    title="🎭 Deepfake Video Detection (Fast)",
+    description="Upload a short MP4 video (under 60MB). This model will detect faces and classify each as REAL or FAKE based on frame analysis."
 )
 
 demo.launch()