diff --git a/src/detectionSoftware/run.py b/src/detectionSoftware/run.py
index c6d80cdf..6f357b58 100644
--- a/src/detectionSoftware/run.py
+++ b/src/detectionSoftware/run.py
@@ -1,10 +1,24 @@
 import sys
 import subprocess
 import threading
-import logging
-from flask import Flask, Response, render_template_string
+import os
+import shutil
+import time
+import glob
+from flask import Flask, Response, render_template_string, send_from_directory
 
-# --- PART 1: ROBUST DETECTION (Unchanged) ---
+# --- CONFIGURATION ---
+HLS_DIR = "/tmp/hls_stream"
+HLS_PLAYLIST = "stream.m3u8"
+CAMERA_1_SERIAL = "40650847" 
+CAMERA_2_SERIAL = "40653314"
+
+# Ensure clean HLS directory
+if os.path.exists(HLS_DIR):
+    shutil.rmtree(HLS_DIR)
+os.makedirs(HLS_DIR)
+
+# --- PART 1: ROBUST DETECTION ---
 def detect_camera_resolution_isolated():
     print("--- Spawning isolated process for detection ---")
     detection_script = """
@@ -39,23 +53,61 @@ except Exception:
 
 CAM_W, CAM_H = detect_camera_resolution_isolated()
 
-# Calculate Grid
+# --- RESOLUTION LOGIC ---
 STREAM_WIDTH = CAM_W
 STREAM_HEIGHT = CAM_H
-TILED_WIDTH = CAM_W * 2
-TILED_HEIGHT = CAM_H
+
+# FIX: We calculate the Full "Virtual" Width
+full_width = CAM_W * 2
+full_height = CAM_H
+
+# FIX: Then we scale it down to something the Encoder (and Browser) can handle.
+# Target width: 1920 (Standard HD width).
+# We calculate height to maintain aspect ratio.
+TILED_WIDTH = 1920
+scale_factor = TILED_WIDTH / full_width
+TILED_HEIGHT = int(full_height * scale_factor)
+
+# Ensure height is even (required for YUV420)
+if TILED_HEIGHT % 2 != 0: TILED_HEIGHT += 1
+
+print(f"Resolution Map: Input {STREAM_WIDTH}x{STREAM_HEIGHT} -> Tiled Output {TILED_WIDTH}x{TILED_HEIGHT}")
+
 
 # --- PART 2: FLASK & GSTREAMER ---
 import gi
 gi.require_version('Gst', '1.0')
 from gi.repository import Gst, GLib
 
-CAMERA_1_SERIAL = "40650847" 
-CAMERA_2_SERIAL = "40653314"
-
 app = Flask(__name__)
-frame_buffer = None
-buffer_lock = threading.Lock()
+
+class PlaylistGenerator(threading.Thread):
+    def __init__(self):
+        super().__init__()
+        self.daemon = True
+
+    def run(self):
+        while True:
+            time.sleep(1.0)
+            files = sorted(glob.glob(os.path.join(HLS_DIR, "*.ts")), key=os.path.getmtime)
+            
+            if len(files) > 6:
+                for f in files[:-6]:
+                    try: os.remove(f)
+                    except: pass
+                files = files[-6:]
+            
+            if not files: continue
+            
+            content = "#EXTM3U\n#EXT-X-VERSION:3\n#EXT-X-TARGETDURATION:2\n#EXT-X-MEDIA-SEQUENCE:0\n"
+            for f in files:
+                filename = os.path.basename(f)
+                content += "#EXTINF:2.000000,\n" + filename + "\n"
+                
+            with open(os.path.join(HLS_DIR, "temp.m3u8"), "w") as f:
+                f.write(content)
+            os.rename(os.path.join(HLS_DIR, "temp.m3u8"), os.path.join(HLS_DIR, HLS_PLAYLIST))
+
 
 class GStreamerPipeline(threading.Thread):
     def __init__(self):
@@ -74,27 +126,7 @@ class GStreamerPipeline(threading.Thread):
         finally:
             self.pipeline.set_state(Gst.State.NULL)
 
-    def on_new_sample(self, sink):
-        sample = sink.emit("pull-sample")
-        if not sample: return Gst.FlowReturn.ERROR
-
-        buffer = sample.get_buffer()
-        success, map_info = buffer.map(Gst.MapFlags.READ)
-        if not success: return Gst.FlowReturn.ERROR
-
-        global frame_buffer
-        with buffer_lock:
-            # Copy memory from GPU buffer to CPU for Flask
-            frame_buffer = bytes(map_info.data)
-
-        buffer.unmap(map_info)
-        return Gst.FlowReturn.OK
-
     def build_pipeline(self):
-        # PERFORMANCE TUNING:
-        # 1. PacketSize=8192: Critical for GigE cameras to reduce CPU interrupts (Enable Jumbo Frames on NIC!)
-        # 2. FrameRate: Capped at 30 to prevent saturating the USB/GigE bus.
-        
         cam_settings = (
             "cam::TriggerMode=Off "
             "cam::AcquisitionFrameRateEnable=true cam::AcquisitionFrameRate=30.0 "
@@ -102,18 +134,16 @@ class GStreamerPipeline(threading.Thread):
             "cam::GainAuto=Continuous "
         )
 
-        # Source 1: Mono -> CPU Convert (Light) -> GPU Upload
         src1 = (
             f"pylonsrc device-serial-number={CAMERA_1_SERIAL} {cam_settings} ! "
             "video/x-raw,format=GRAY8 ! "
-            "videoconvert ! "             # Light CPU load: Gray -> I420
+            "videoconvert ! "
             "video/x-raw,format=I420 ! "
-            "nvvideoconvert compute-hw=1 ! " # Upload to GPU Memory (NVMM)
-            "video/x-raw(memory:NVMM) ! "    # Explicitly state we want NVMM
+            "nvvideoconvert compute-hw=1 ! "
+            "video/x-raw(memory:NVMM) ! "
             "m.sink_0 "
         )
 
-        # Source 2
         src2 = (
             f"pylonsrc device-serial-number={CAMERA_2_SERIAL} {cam_settings} ! "
             "video/x-raw,format=GRAY8 ! "
@@ -124,49 +154,82 @@ class GStreamerPipeline(threading.Thread):
             "m.sink_1 "
         )
 
-        # Muxer -> Tiler -> Hardware Encoder
-        # nvjpegenc is the key here. It uses the dedicated JPEG block on the Jetson.
+        # Processing
+        # The Tiler accepts the full resolution inputs, but OUTPUTS the scaled-down resolution (TILED_WIDTH).
+        # This is extremely efficient because the scaling happens on the GPU.
         processing = (
             f"nvstreammux name=m batch-size=2 width={STREAM_WIDTH} height={STREAM_HEIGHT} live-source=1 ! "
             f"nvmultistreamtiler width={TILED_WIDTH} height={TILED_HEIGHT} rows=1 columns=2 ! "
-            "nvvideoconvert compute-hw=1 ! " # Ensure we are ready for encoding
-            "video/x-raw(memory:NVMM) ! "    # Keep in NVMM
-            "nvjpegenc quality=85 ! "        # HARDWARE ENCODE
-            "appsink name=sink emit-signals=True sync=False max-buffers=1 drop=True"
+            "nvvideoconvert compute-hw=1 ! "
+            "video/x-raw(memory:NVMM) ! "
+            f"nvv4l2h264enc bitrate=5000000 profile=2 preset-level=1 ! " # Reduced bitrate for 1080p width
+            "h264parse config-interval=1 ! "
+            "queue ! "
+            f"splitmuxsink location={HLS_DIR}/segment%05d.ts muxer=mpegtsmux max-size-time=2000000000 max-files=10"
         )
 
         pipeline_str = f"{src1} {src2} {processing}"
         
-        print(f"Launching CUDA-Accelerated Pipeline...")
+        print(f"Launching Final Scaled Pipeline...")
         self.pipeline = Gst.parse_launch(pipeline_str)
-        
-        appsink = self.pipeline.get_by_name("sink")
-        appsink.connect("new-sample", self.on_new_sample)
 
 # --- Flask Routes ---
+
 @app.route('/')
 def index():
     return render_template_string('''
-        <html><body style="background:#111; color:white; text-align:center;">
-            <h1>Basler CUDA Feed</h1>
-            <img src="{{ url_for('video_feed') }}" style="border: 2px solid #555; max-width:95%;">
-        </body></html>
+        <html>
+        <head>
+            <script src="https://cdn.jsdelivr.net/npm/hls.js@latest"></script>
+            <style>body { background: #111; text-align: center; color: white; }</style>
+        </head>
+        <body>
+            <h1>Basler AVC Feed</h1>
+            <video id="video" controls autoplay muted style="width: 90%; border: 2px solid #4CAF50;"></video>
+            <script>
+                var video = document.getElementById('video');
+                var videoSrc = "/hls/stream.m3u8";
+                
+                function loadStream() {
+                    if (Hls.isSupported()) {
+                        var hls = new Hls({
+                            maxBufferLength: 5,
+                            liveSyncDurationCount: 3,
+                            lowLatencyMode: true
+                        });
+                        hls.loadSource(videoSrc);
+                        hls.attachMedia(video);
+                        hls.on(Hls.Events.MANIFEST_PARSED, function() { video.play(); });
+                        hls.on(Hls.Events.ERROR, function (event, data) {
+                             if (data.fatal) hls.startLoad();
+                        });
+                    }
+                }
+                
+                var checkExist = setInterval(function() {
+                   fetch(videoSrc).then(function(response) {
+                       if (response.status == 200) {
+                           clearInterval(checkExist);
+                           loadStream();
+                       }
+                   });
+                }, 1000);
+            </script>
+        </body>
+        </html>
     ''')
 
-@app.route('/video_feed')
-def video_feed():
-    def generate():
-        while True:
-            with buffer_lock:
-                if frame_buffer:
-                    yield (b'--frame\r\n'
-                           b'Content-Type: image/jpeg\r\n\r\n' + frame_buffer + b'\r\n')
-            GLib.usleep(25000) # ~40 FPS cap for web
-
-    return Response(generate(), mimetype='multipart/x-mixed-replace; boundary=frame')
+@app.route('/hls/<path:filename>')
+def serve_hls(filename):
+    return send_from_directory(HLS_DIR, filename)
 
 if __name__ == "__main__":
     gst_thread = GStreamerPipeline()
     gst_thread.daemon = True
     gst_thread.start()
+    
+    pl_thread = PlaylistGenerator()
+    pl_thread.start()
+    
+    print("Waiting for HLS segments...")
     app.run(host='0.0.0.0', port=5000, debug=False, threaded=True)