Added deepfake video detection

.gitignore

@@ -1,3 +1,4 @@
 __pycache__/
 node_modules/
-.env
+.env
+.venv/

app.py

@@ -31,23 +31,53 @@ async def analyze_news(news: NewsInput):
     }
 
     
+# @app.post("/detect-deepfake")
+# async def detect_deepfake(image: UploadFile = File(...)):
+#     try:
+#         # Save uploaded image temporarily
+#         with NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
+#             contents = await image.read()
+#             temp_file.write(contents)
+#             temp_file_path = temp_file.name
+
+#         # Use your existing deepfake detection function
+#         from deepfake2.testing2 import predict_image  # Use your existing function
+#         result = predict_image(temp_file_path)
+        
+#         # Clean up temp file
+#         os.remove(temp_file_path)
+        
+#         return result
+    # except Exception as e:
+    #     return {"error": str(e)}, 500
+
 @app.post("/detect-deepfake")
-async def detect_deepfake(image: UploadFile = File(...)):
+async def detect_deepfake(file: UploadFile = File(...)):
     try:
-        # Save uploaded image temporarily
-        with NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
-            contents = await image.read()
+        # Save uploaded file temporarily
+        with NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_file:
+            contents = await file.read()
             temp_file.write(contents)
             temp_file_path = temp_file.name
 
-        # Use your existing deepfake detection function
-        from deepfake2.testing2 import predict_image  # Use your existing function
-        result = predict_image(temp_file_path)
+        # Import functions from testing2.py
+        from deepfake2.testing2 import predict_image, predict_video
+
+        # Use appropriate function based on file type
+        if file.filename.lower().endswith('.mp4'):
+            result = predict_video(temp_file_path)
+            file_type = "video"
+        else:
+            result = predict_image(temp_file_path)
+            file_type = "image"
         
         # Clean up temp file
         os.remove(temp_file_path)
         
-        return result
-        
+        return {
+            "result": result,
+            "file_type": file_type
+        }
+            
     except Exception as e:
         return {"error": str(e)}, 500

deepfake2/testing2.py

@@ -24,6 +24,46 @@ def predict_image(img_path):
     prediction = model.predict(img_array)
     return "Fake" if prediction[0][0] > 0.5 else "Real"
 
+# Add this function for video analysis
+def predict_video(video_path):
+    if not os.path.exists(video_path):
+        return "Video path does not exist."
+        
+    cap = cv2.VideoCapture(video_path)
+    fake_frames = 0
+    total_frames = 0
+    
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+            
+        # Convert frame to RGB and resize
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame_resized = cv2.resize(frame_rgb, (img_height, img_width))
+        
+        # Prepare frame for prediction
+        frame_array = image.img_to_array(frame_resized) / 255.0
+        frame_array = np.expand_dims(frame_array, axis=0)
+        
+        # Predict frame
+        prediction = model.predict(frame_array)
+        if prediction[0][0] > 0.5:
+            fake_frames += 1
+        total_frames += 1
+        
+    cap.release()
+    
+    # Calculate percentage of fake frames
+    fake_percentage = (fake_frames / total_frames) * 100 if total_frames > 0 else 0
+    
+    return {
+        "prediction": "Fake" if fake_percentage > 50 else "Real",
+        "confidence_score": round(fake_percentage / 100, 2),
+        "frames_analyzed": total_frames,
+        "fake_frames_detected": fake_frames
+    }
+
 # Metadata analysis
 def check_metadata(img_path):
     try:

nexus-frontend/src/pages/deepfake.tsx

@@ -60,6 +60,14 @@ const DeepfakeDetection = () => {
   const [selectedImage, setSelectedImage] = useState(null);
   const [result, setResult] = useState(null);
   const [loading, setLoading] = useState(false);
+  const [selectedVideo, setSelectedVideo] = useState(null);
+  const [fileType, setFileType] = useState(null);
+
+  const handleVideoSelect = (event) => {
+    const file = event.target.files[0];
+    setSelectedVideo(file);
+    setFileType('video');
+  };
 
   const handleImageSelect = (event) => {
     const file = event.target.files[0];
@@ -67,10 +75,12 @@ const DeepfakeDetection = () => {
   };
 
   const handleUpload = async () => {
-    if (!selectedImage) return;
+    const fileToUpload = fileType === 'video' ? selectedVideo : selectedImage;
+    if (!fileToUpload) return;
+    
     setLoading(true);
     const formData = new FormData();
-    formData.append('image', selectedImage);
+    formData.append('file', fileToUpload);
 
     try {
       const response = await fetch('http://localhost:8000/detect-deepfake', {
@@ -99,21 +109,39 @@ const DeepfakeDetection = () => {
           
           <div className="space-y-6">
             <div className="flex flex-col items-center">
-              <input
-                accept="image/*"
-                className="hidden"
-                id="image-upload"
-                type="file"
-                onChange={handleImageSelect}
-              />
-              <label htmlFor="image-upload">
-                <div className="px-6 py-3 rounded-lg bg-blue-500 hover:bg-blue-600 text-white cursor-pointer flex items-center gap-2">
-                  <CloudUploadIcon />
-                  Upload Image
+              <div className="flex gap-4">
+                <input
+                  accept="image/*"
+                  className="hidden"
+                  id="image-upload"
+                  type="file"
+                  onChange={(e) => {
+                    handleImageSelect(e);
+                    setFileType('image');
+                  }}
+                />
+                <label htmlFor="image-upload">
+                  <div className="px-6 py-3 rounded-lg bg-blue-500 hover:bg-blue-600 text-white cursor-pointer flex items-center gap-2">
+                    <CloudUploadIcon />
+                    Upload Image
+                  </div>
+                </label>
+                  <input
+                    accept="video/mp4"
+                    className="hidden"
+                    id="video-upload"
+                    type="file"
+                    onChange={handleVideoSelect}
+                  />
+                  <label htmlFor="video-upload">
+                    <div className="px-6 py-3 rounded-lg bg-green-500 hover:bg-green-600 text-white cursor-pointer flex items-center gap-2">
+                      <CloudUploadIcon />
+                      Upload MP4 Video
+                    </div>
+                  </label>
                 </div>
-              </label>
               
-              {selectedImage && (
+              {selectedImage && fileType === 'image' && (
                 <div className="mt-4 text-center">
                   <p className="text-blue-300">Selected: {selectedImage.name}</p>
                   <img 
@@ -123,10 +151,23 @@ const DeepfakeDetection = () => {
                   />
                 </div>
               )}
+
+              {selectedVideo && fileType === 'video' && (
+                <div className="mt-4 text-center">
+                  <p className="text-blue-300">Selected Video: {selectedVideo.name}</p>
+                  <video 
+                    className="max-w-md mt-4 rounded-lg mx-auto"
+                    controls
+                  >
+                    <source src={URL.createObjectURL(selectedVideo)} type={selectedVideo.type} />
+                    Your browser does not support the video tag.
+                  </video>
+                </div>
+              )}
               
               <button
                 onClick={handleUpload}
-                disabled={!selectedImage || loading}
+                disabled={!selectedImage && !selectedVideo || loading}
                 className="mt-4 px-6 py-2 rounded-lg bg-blue-500 hover:bg-blue-600 text-white disabled:opacity-50"
               >
                 Analyze