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app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import cv2
+
+# Load YOLOv8 model once at startup
+model = YOLO("yolov8n.pt")  # nano model — fast and free, good for demo
+
+def count_people(image):
+    if image is None:
+        return None, "No image uploaded."
+
+    # Convert PIL image to numpy array (BGR for OpenCV)
+    img_array = np.array(image)
+    img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+
+    # Run YOLO inference — only detect class 0 (person)
+    results = model(img_bgr, classes=[0], conf=0.4)
+
+    # Get detections
+    result = results[0]
+    boxes = result.boxes
+    count = len(boxes)
+
+    # Draw bounding boxes on the image
+    annotated = img_bgr.copy()
+    for box in boxes:
+        x1, y1, x2, y2 = map(int, box.xyxy[0])
+        conf = float(box.conf[0])
+        cv2.rectangle(annotated, (x1, y1), (x2, y2), (0, 200, 100), 2)
+        label = f"Person {conf:.0%}"
+        cv2.putText(annotated, label, (x1, y1 - 8),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.55, (0, 200, 100), 2)
+
+    # Add count overlay at top
+    overlay_text = f"Total People Detected: {count}"
+    cv2.rectangle(annotated, (0, 0), (len(overlay_text) * 14 + 20, 45), (0, 0, 0), -1)
+    cv2.putText(annotated, overlay_text, (10, 30),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.85, (255, 255, 255), 2)
+
+    # Convert back to RGB for display
+    annotated_rgb = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
+    output_image = Image.fromarray(annotated_rgb)
+
+    # Summary text
+    if count == 0:
+        summary = "👤 No people detected in this image."
+    elif count == 1:
+        summary = "👤 1 person detected."
+    else:
+        summary = f"👥 {count} people detected."
+
+    return output_image, summary
+
+
+# --- Gradio UI ---
+with gr.Blocks(
+    title="People Counter — YOLOv8",
+    theme=gr.themes.Base(
+        primary_hue="emerald",
+        neutral_hue="slate",
+        font=gr.themes.GoogleFont("DM Sans"),
+    ),
+    css="""
+        body { background: #0f1117; }
+        .gradio-container { max-width: 860px !important; margin: auto; }
+        #title { text-align: center; padding: 2rem 0 0.5rem; }
+        #title h1 { font-size: 2.2rem; font-weight: 800; color: #f1f5f9; letter-spacing: -0.5px; }
+        #title p  { color: #94a3b8; font-size: 1rem; margin-top: 0.3rem; }
+        #count-box textarea {
+            font-size: 1.3rem !important;
+            font-weight: 700 !important;
+            text-align: center !important;
+            color: #34d399 !important;
+            background: #1e293b !important;
+            border: 1px solid #334155 !important;
+            border-radius: 10px !important;
+        }
+        .upload-btn { background: #10b981 !important; color: white !important; font-weight: 700 !important; }
+        footer { display: none !important; }
+    """
+) as demo:
+
+    with gr.Column(elem_id="title"):
+        gr.HTML("<h1>🔍 People Counter</h1>")
+        gr.HTML("<p>Upload an image — YOLOv8 will detect and count every person in it.</p>")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Upload Image", height=380)
+            run_btn = gr.Button("Count People →", variant="primary", elem_classes="upload-btn")
+
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Detection Result", height=380)
+            output_text = gr.Textbox(
+                label="Count",
+                interactive=False,
+                elem_id="count-box",
+                lines=1
+            )
+
+    run_btn.click(
+        fn=count_people,
+        inputs=input_image,
+        outputs=[output_image, output_text]
+    )
+
+    gr.HTML("""
+        <div style='text-align:center; color:#475569; font-size:0.8rem; padding: 1.5rem 0 0.5rem;'>
+            Powered by <strong>Ultralytics YOLOv8</strong> · Model: yolov8n · Class: Person · Conf: 0.4
+        </div>
+    """)
+
+demo.launch()

requirements.txt

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+ultralytics==8.3.0
+gradio==4.44.0
+opencv-python-headless==4.10.0.84
+Pillow==10.4.0
+numpy==1.26.4
+torch==2.4.0
+torchvision==0.19.0