app.py

import gradio as gr
import torch
import cv2
from ultralytics import YOLO

# Load YOLO models
def safe_load_yolo_model(path):
    torch.serialization.add_safe_globals([torch, 'ultralytics.nn.tasks.DetectionModel'])
    return YOLO(path)

# Dictionary of model paths
model_paths = {
    'YOLOv11': './data/yolo11n.pt',
    'Crack & Pothole Detector': './data/best.pt',
    'Toll gates': './data/best2.pt'
    
}

# Load models into memory
models = {name: safe_load_yolo_model(path) for name, path in model_paths.items()}

# Assign colors for each model
model_colors = {
    'YOLOv11': (0, 255, 0),
    'Crack & Pothole Detector': (255, 0, 0),
    'Toll gates': (0, 0, 255)
}

def process_video(video, selected_model):
    cap = cv2.VideoCapture(video)
    fps = cap.get(cv2.CAP_PROP_FPS)
    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    out = cv2.VideoWriter('output_video.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))

    use_models = models if selected_model == 'All' else {selected_model: models[selected_model]}

    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        for model_name, model in use_models.items():
            results = model(frame)

            for result in results:
                for box in result.boxes:
                    x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
                    class_id = int(box.cls[0])
                    label = f"{model.names[class_id]} - {box.conf[0]:.2f}"
                    color = model_colors.get(model_name, (0, 255, 255))
                    cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
                    cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2)

        out.write(frame)

    cap.release()
    out.release()
    return 'output_video.mp4'

# Gradio Interface
iface = gr.Interface(
    fn=process_video,
    inputs=[
        gr.Video(label="Upload a Video"),
        gr.Dropdown(
            choices=["All"] + list(model_paths.keys()),
            label="Select Model(s)",
            value="All"
        )
    ],
    outputs=gr.Video(label="Processed Output"),
    live=False,
    title="Multi-Model YOLOv8 Video Inference"
)

iface.launch()