Update app.py

app.py

@@ -5,6 +5,7 @@ import torch.nn as nn
 import numpy as np
 from torchvision import transforms
 import os
+import time
 
 # --- 1. MODEL ARCHITECTURE ---
 class LDobjModel(nn.Module):
@@ -43,78 +44,127 @@ transform = transforms.Compose([
     transforms.ToTensor()
 ])
 
-# --- 3. PROCESSING LOGIC ---
-def analyze_video(input_video_path):
+# --- 3. ADVANCED PROCESSING LOGIC (With Progress & Analytics) ---
+def analyze_video(input_video_path, sensitivity, progress=gr.Progress()):
     if not input_video_path:
-        return None
+        return None, "⚠️ Please upload a video first."
 
+    start_time = time.time()
+    
     cap = cv2.VideoCapture(input_video_path)
     width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
     height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
     fps    = cap.get(cv2.CAP_PROP_FPS)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
     
     raw_output = "temp_raw.mp4"
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     out = cv2.VideoWriter(raw_output, fourcc, fps, (width, height))
     morph_kernel = np.ones((5, 5), np.uint8)
 
+    # Calculate threshold based on user slider (e.g., 10% = 0.10)
+    drift_threshold = width * (sensitivity / 100.0)
+    frame_count = 0
+    alerts_triggered = 0
+
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret: break
 
+        # Report progress to the UI
+        frame_count += 1
+        if frame_count % 5 == 0:
+            progress(frame_count / total_frames, desc=f"Processing AI Vision: Frame {frame_count}/{total_frames}")
+
+        # AI Prediction
         input_img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         img_tensor = transform(input_img).unsqueeze(0).to(device)
         with torch.no_grad():
             pred = model(img_tensor).squeeze().numpy()
 
+        # Mask Cleaning
         mask = (pred > 0.5).astype(np.uint8)
         mask_full = cv2.resize(mask, (width, height), interpolation=cv2.INTER_NEAREST)
         mask_full = cv2.morphologyEx(mask_full, cv2.MORPH_OPEN, morph_kernel)
 
+        # Departure Alert Logic
         moments = cv2.moments(mask_full[int(height*0.75):, :])
-        if moments["m00"] > 0 and abs(int(moments["m10"] / moments["m00"]) - width // 2) > (width * 0.1):
+        if moments["m00"] > 0 and abs(int(moments["m10"] / moments["m00"]) - width // 2) > drift_threshold:
+            alerts_triggered += 1
             overlay = frame.copy()
             overlay[mask_full > 0] = (0, 0, 255)
             frame = cv2.addWeighted(frame, 0.7, overlay, 0.3, 0)
-            cv2.putText(frame, "LANE DEPARTURE", (width//10, 80), cv2.FONT_HERSHEY_DUPLEX, 1.5, (0, 0, 255), 3)
+            
+            # Draw sleek UI elements
+            cv2.rectangle(frame, (0, 0), (width, 120), (0, 0, 0), -1) # Black header bar
+            cv2.putText(frame, "LANE DEPARTURE DETECTED", (30, 80), cv2.FONT_HERSHEY_DUPLEX, 1.5, (0, 0, 255), 3)
 
         out.write(frame)
 
-    cap.release(); out.release()
+    cap.release()
+    out.release()
+    
+    progress(0.95, desc="Optimizing Video for Web...")
     web_output = "ldobj_final.mp4"
-    os.system(f"ffmpeg -y -i {raw_output} -c:v libx264 -pix_fmt yuv420p -movflags +faststart {web_output}")
-    return web_output
+    os.system(f"ffmpeg -y -i {raw_output} -c:v libx264 -preset fast -pix_fmt yuv420p -movflags +faststart {web_output}")
+    
+    # Calculate Telemetry
+    process_time = time.time() - start_time
+    avg_fps = frame_count / process_time
+    
+    telemetry_report = (
+        f"✅ Analysis Complete\n"
+        f"------------------------\n"
+        f"⏱️ Processing Time: {process_time:.1f} seconds\n"
+        f"🎞️ Total Frames: {frame_count}\n"
+        f"🚀 AI Speed: {avg_fps:.1f} FPS\n"
+        f"🚨 Alerts Triggered: {alerts_triggered} frames"
+    )
+    
+    return web_output, telemetry_report
 
-# --- 4. FRONTEND DESIGN (Corrected for Gradio 6.0) ---
+# --- 4. ULTIMATE FRONTEND DESIGN ---
 custom_css = """
-#video-container { min-height: 400px; }
-.gradio-container { background-color: #f7f9fc; }
+#video-in, #video-out { min-height: 450px; border-radius: 10px; border: 1px solid #333; }
+.gradio-container { max-width: 1200px !important; margin: auto; }
 footer { visibility: hidden; }
+.glow-title { color: #ff4a4a; text-shadow: 0px 0px 15px rgba(255, 74, 74, 0.5); text-align: center; margin-bottom: 5px; }
+.sub-title { text-align: center; color: #888; margin-top: 0px; margin-bottom: 30px; }
 """
 
-# Theme and CSS removed from constructor as per the warning
-with gr.Blocks() as app:
-    gr.HTML("<h1 style='text-align: center; color: #d32f2f;'>🚗 LDobj Safety Interface</h1>")
-    gr.HTML("<p style='text-align: center;'>AI-Powered Lane Departure Detection & Alert System</p>")
+with gr.Blocks(theme=gr.themes.Slate(primary_hue="red")) as app:
+    gr.HTML("<h1 class='glow-title'>🛡️ LDobj ADAS Command Center</h1>")
+    gr.HTML("<h3 class='sub-title'>Advanced Driver Assistance System • Neural Lane Tracking</h3>")
     
     with gr.Group():
         with gr.Row():
-            with gr.Column(scale=1):
-                # mirror_webcam argument removed to fix TypeError
-                video_in = gr.Video(label="Source Dashcam Feed")
-                run_btn = gr.Button("START AI ANALYSIS", variant="primary")
+            # LEFT COLUMN: Controls
+            with gr.Column(scale=4):
+                gr.Markdown("### 1. Input Source")
+                video_in = gr.Video(label="Dashcam Feed", elem_id="video-in")
+                
+                gr.Markdown("### 2. AI Parameters")
+                sensitivity_slider = gr.Slider(
+                    minimum=5, maximum=25, value=10, step=1, 
+                    label="Drift Sensitivity Threshold (%)", 
+                    info="Lower % = Stricter alerts. Higher % = Allows more drift before alerting."
+                )
+                
+                run_btn = gr.Button("INITIALIZE SCAN", variant="primary", size="lg")
             
-            with gr.Column(scale=1):
-                video_out = gr.Video(label="LDobj Alert Output", interactive=False, autoplay=True)
-
-    gr.Markdown("---")
-    gr.Markdown("### How it works\n1. **Monitor:** System analyzes lanes in the background.\n2. **Active Alert:** Warnings only appear when a lane departure is detected.")
-
-    run_btn.click(fn=analyze_video, inputs=video_in, outputs=video_out)
+            # RIGHT COLUMN: Output
+            with gr.Column(scale=5):
+                gr.Markdown("### Live Output Feed")
+                video_out = gr.Video(label="LDobj Processed Feed", interactive=False, autoplay=True, elem_id="video-out")
+                
+                gr.Markdown("### System Telemetry")
+                telemetry_out = gr.Textbox(label="Analytics Console", lines=6, interactive=False)
+
+    run_btn.click(
+        fn=analyze_video, 
+        inputs=[video_in, sensitivity_slider], 
+        outputs=[video_out, telemetry_out]
+    )
 
 if __name__ == "__main__":
-    # Theme and CSS moved here to comply with Gradio 6.0
-    app.launch(
-        theme=gr.themes.Default(primary_hue="red"),
-        css=custom_css
-    )
+    app.launch(css=custom_css)