app.py

import gradio as gr
from ultralytics import YOLO
from PIL import Image

# Load YOLOv8 model
model = YOLO("yolov8s.pt")  # Ensure it's in the same folder as app.py

# Function for image upload detection
def detect_objects(image, conf_threshold):
    results = model(image, conf=conf_threshold)
    result_image = results[0].plot()

    detections = results[0].boxes
    if detections is not None and len(detections) > 0:
        labels = []
        for box in detections:
            cls = int(box.cls)
            conf = float(box.conf)
            if conf >= conf_threshold:
                name = model.names[cls]
                labels.append(f"{name} ({conf*100:.2f}%)")
        confidence_text = "\n".join(labels) if labels else "No objects above threshold."
    else:
        confidence_text = "No objects detected."

    return Image.fromarray(result_image), confidence_text


# Function for live webcam detection
def detect_webcam(frame, conf_threshold):
    results = model(frame, conf=conf_threshold)
    result_frame = results[0].plot()
    return Image.fromarray(result_frame)


# UI setup
title = "YOLOv8s Object Detection BY ATUL 🚀"
description = """
Detect objects using YOLOv8s.  
Adjust the **confidence threshold** in real time to control detection sensitivity.  
You can upload an image or use your webcam.  
Built with ❤️ using Ultralytics YOLO + Gradio.
"""

with gr.Blocks(title=title) as app:
    gr.Markdown(f"# {title}")
    gr.Markdown(description)

    with gr.Tab("📸 Image Upload"):
        conf_slider = gr.Slider(0.1, 1.0, value=0.5, step=0.05, label="Confidence Threshold")
        image_input = gr.Image(type="pil", label="Upload Image")
        image_output = gr.Image(label="Detected Objects")
        conf_output = gr.Textbox(label="Detected Objects (with Confidence)", interactive=False)

        gr.Button("Detect").click(
            fn=detect_objects,
            inputs=[image_input, conf_slider],
            outputs=[image_output, conf_output]
        )

    with gr.Tab("🎥 Live Camera"):
        conf_slider_live = gr.Slider(0.1, 1.0, value=0.5, step=0.05, label="Confidence Threshold")
        webcam_input = gr.Image(label="Webcam Feed", sources=["webcam"], streaming=True)
        webcam_output = gr.Image(label="Live Detection")

        webcam_input.stream(
            fn=detect_webcam,
            inputs=[webcam_input, conf_slider_live],
            outputs=webcam_output
        )

if __name__ == "__main__":
    app.launch()