add analytics dashboard: per-class chart + time-series chart

app.py

@@ -3,7 +3,7 @@ from src.detector import process_video
 
 def run(video_input):
     if video_input is None:
-        return None, None, "⚠️  Please upload a video first."
+        return None, None, "⚠️  Please upload a video first.", None, None
     return process_video(video_input)
 
 
@@ -14,6 +14,8 @@ demo = gr.Interface(
         gr.Video(label="Annotated Output (with tracking + counting lines)"),
         gr.File(label="Crossings CSV"),
         gr.Markdown(label="Summary"),
+        gr.Plot(label="📊 Crossings by Class"),
+        gr.Plot(label="⏱️  Crossings Over Time"),
     ],
     title="🚦 Traffic Analytics Pipeline",
     description=(

requirements.txt

@@ -1,3 +1,5 @@
 gradio>=4.0
 ultralytics>=8.0
 opencv-python-headless>=4.8
+
+matplotlib>=3.7

src/detector.py

@@ -4,6 +4,10 @@ import csv
 from ultralytics import YOLO
 import tempfile
 import re
+import matplotlib
+matplotlib.use("Agg")   # non-interactive backend (required for server / Gradio)
+import matplotlib.pyplot as plt
+from collections import Counter, defaultdict
 
 model = YOLO('yolov8n.pt')
 
@@ -43,6 +47,61 @@ def detect(frame):
     return detections, annotated      
 
 
+def make_per_class_chart(crossings):
+    """Bar chart: count of crossings per vehicle class."""
+    class_counts = Counter(c["class"] for c in crossings)
+    if not class_counts:
+          # Empty fallback
+        fig, ax = plt.subplots(figsize=(6, 4))
+        ax.text(0.5, 0.5, "No crossings detected", ha="center", va="center", fontsize=14)
+        ax.axis("off")
+        return fig  
+
+    classes = list(class_counts.keys())
+    counts = list(class_counts.values())
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+    ax.bar(classes, counts, color=["#3498db", "#e67e22", "#2ecc71", "#9b59b6"][:len(classes)])
+    ax.set_title("Crossings by Vehicle Class", fontsize=13, fontweight="bold")
+    ax.set_xlabel("Vehicle Class")
+    ax.set_ylabel("Number of Crossings")
+    for i, v in enumerate(counts):
+        ax.text(i, v + 0.1, str(v), ha="center", fontweight="bold")
+    plt.tight_layout()
+    return fig
+
+def make_time_series_chart(crossings, fps, total_frames):
+    """Time-series: crossings per 5-second window."""
+    if not crossings:
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.text(0.5, 0.5, "No crossings detected", ha="center", va="center", fontsize=14)
+        ax.axis("off")
+        return fig
+  
+    bucket_size_sec = 5
+    total_duration_sec = total_frames / fps if fps > 0 else 0
+
+    buckets = defaultdict(int)
+    for c in crossings:
+        bucket = int(c["timestamp_sec"] // bucket_size_sec)
+        buckets[bucket] += 1  
+
+    max_bucket = int(total_duration_sec // bucket_size_sec) + 1
+    x = list(range(max_bucket))
+    y = [buckets.get(b, 0) for b in x]
+    x_labels = [f"{b * bucket_size_sec}s" for b in x]
+
+    fig, ax = plt.subplots(figsize=(8, 4))
+    ax.plot(x_labels, y, marker="o", linewidth=2, color="#e67e22")
+    ax.fill_between(range(len(x_labels)), y, alpha=0.2, color="#e67e22")
+    ax.set_title("Crossings Over Time (5-second windows)", fontsize=13, fontweight="bold")
+    ax.set_xlabel("Time into clip")
+    ax.set_ylabel("Crossings in window")
+    ax.grid(True, alpha=0.3)
+    plt.xticks(rotation=45, ha="right")
+    plt.tight_layout()
+    return fig
+
 def process_video(input_video_path):
     output_dir = tempfile.mkdtemp(prefix="traffic_")
     output_video_path = os.path.join(output_dir, "traffic_tracked.mp4")
@@ -161,7 +220,11 @@ def process_video(input_video_path):
     _Built with YOLOv8 + ByteTrack. See repo for methodology._
     """
 
-    return output_video_path, output_csv_path, summary
+    per_class_chart = make_per_class_chart(crossings)
+    time_series_chart = make_time_series_chart(crossings, fps, frame_count)
+
+    return output_video_path, output_csv_path, summary, per_class_chart, time_series_chart
+  
 
 
 if __name__ == "__main__":
@@ -170,7 +233,7 @@ if __name__ == "__main__":
     project_dir = os.path.dirname(script_dir)
     VIDEO_PATH = os.path.join(project_dir, "data", "traffic.mp4")
 
-    annotated, csv_out, summary = process_video(VIDEO_PATH)
+    annotated, csv_out, summary, _, _ = process_video(VIDEO_PATH)
 
     print(f"\n✅ Done!")              
     print(f"📹 Annotated video: {annotated}")
@@ -183,4 +246,7 @@ if __name__ == "__main__":
         if m:
             auto = int(m.group(1))
             acc = round(100 * (1 - abs(auto - MANUAL_UNIQUE) / MANUAL_UNIQUE), 1)
-            print(f"\n🎯 Unique vehicle accuracy vs manual ({MANUAL_UNIQUE}): {acc}%")
+            print(f"\n🎯 Unique vehicle accuracy vs manual ({MANUAL_UNIQUE}): {acc}%")
+
+
+