app.py

import gradio as gr
import mlflow
import os
import time
import tempfile
from ultralytics import YOLO
from PIL import Image
import cv2
import numpy as np

# MLflow Configuration using Secrets
tracking_uri = os.getenv("MLFLOW_TRACKING_URI")
username = os.getenv("MLFLOW_TRACKING_USERNAME")
password = os.getenv("MLFLOW_TRACKING_PASSWORD")

if not all([tracking_uri, username, password]):
    raise ValueError("MLflow Secrets are not configured! Go to Space Settings and verify the names")

os.environ["MLFLOW_TRACKING_URI"] = tracking_uri
os.environ["MLFLOW_TRACKING_USERNAME"] = username
os.environ["MLFLOW_TRACKING_PASSWORD"] = password

mlflow.set_experiment("YOLOv12s_Inference_Logs")
print("MLflow configured successfully using secrets!")

# Load Model
model = YOLO("Yolo12s.pt")

# Inference with Full MLflow Tracking
def run_inference(media_file, media_type):
    if media_file is None:
        return None, None, None, None, "Please upload a file first"
    
    media_path = media_file.name
    
    with mlflow.start_run(run_name=f"Inference_{int(time.time())}") as run:
        mlflow.log_param("media_type", media_type)
        mlflow.log_param("model", "YOLOv12s")
        mlflow.log_param("timestamp", time.strftime("%Y-%m-%d %H:%M:%S"))
        
        if media_type == "Image":
            img = Image.open(media_path).convert("RGB")
            img_array = np.array(img)
            results = model(img_array)[0]
            annotated = results.plot()
            output_img = Image.fromarray(annotated[..., ::-1])
            
            with tempfile.TemporaryDirectory() as tmpdir:
                in_path = os.path.join(tmpdir, "input.jpg")
                out_path = os.path.join(tmpdir, "output.jpg")
                img.save(in_path)
                output_img.save(out_path)
                mlflow.log_artifact(in_path, artifact_path="input")
                mlflow.log_artifact(out_path, artifact_path="output")
            
            detections = len(results.boxes) if results.boxes is not None else 0
            mlflow.log_metric("detections_count", detections)
            
            return img, output_img, None, None, f"Detection Complete! Objects Detected: {detections}"
        
        else:
            cap = cv2.VideoCapture(media_path)
            fps = cap.get(cv2.CAP_PROP_FPS) or 30
            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            
            output_video = "output_video.mp4"
            writer = cv2.VideoWriter(output_video, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
            
            frame_count = 0
            total_detections = 0
            
            while cap.isOpened():
                ret, frame = cap.read()
                if not ret:
                    break
                
                results = model(frame)[0]
                annotated = results.plot()
                writer.write(annotated)
                
                frame_count += 1
                total_detections += len(results.boxes) if results.boxes is not None else 0
            
            cap.release()
            writer.release()
            
            mlflow.log_artifact(media_path, artifact_path="input_video")
            mlflow.log_artifact(output_video, artifact_path="output_video")
            mlflow.log_metric("frames_processed", frame_count)
            mlflow.log_metric("total_detections", total_detections)
            mlflow.log_metric("avg_detections_per_frame", total_detections / frame_count if frame_count > 0 else 0)
            
            avg_det = total_detections / frame_count if frame_count > 0 else 0
            result_message = f"Video Processing Complete! Frames: {frame_count}, Total Detections: {total_detections}, Average per Frame: {avg_det:.2f}"
            
            result_video = output_video
            
            return None, None, media_path, result_video, result_message

# Modern Aesthetic UI CSS
css = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700&display=swap');

* {
    font-family: 'Inter', sans-serif;
}

body {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
}

.gradio-container {
    max-width: 1400px !important;
    margin: 40px auto !important;
    background: rgba(255, 255, 255, 0.95) !important;
    border-radius: 24px !important;
    box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3) !important;
    padding: 0 !important;
    overflow: hidden !important;
}

.header-container {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    padding: 48px 40px;
    text-align: center;
    border-radius: 24px 24px 0 0;
}

.header-container h1 {
    color: white !important;
    font-size: 2.8em !important;
    font-weight: 700 !important;
    margin: 0 0 12px 0 !important;
    text-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}

.header-container p {
    color: rgba(255, 255, 255, 0.95) !important;
    font-size: 1.1em !important;
    margin: 0 !important;
}

.main-content {
    padding: 48px 40px;
}

.file-upload-area {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    border-radius: 16px;
    padding: 32px;
    border: 2px dashed #667eea;
    transition: all 0.3s ease;
}

.file-upload-area:hover {
    border-color: #764ba2;
    transform: translateY(-2px);
    box-shadow: 0 8px 16px rgba(102, 126, 234, 0.2);
}

button.primary {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
    border: none !important;
    color: white !important;
    font-weight: 600 !important;
    font-size: 1.1em !important;
    padding: 16px 48px !important;
    border-radius: 12px !important;
    cursor: pointer !important;
    transition: all 0.3s ease !important;
    box-shadow: 0 4px 12px rgba(102, 126, 234, 0.4) !important;
}

button.primary:hover {
    transform: translateY(-2px) !important;
    box-shadow: 0 8px 20px rgba(102, 126, 234, 0.6) !important;
}

.radio-group label {
    background: white;
    padding: 12px 24px;
    border-radius: 8px;
    border: 2px solid #e5e7eb;
    cursor: pointer;
    transition: all 0.3s ease;
}

.radio-group label:hover {
    border-color: #667eea;
    background: #f5f7fa;
}

.output-image, .output-video {
    border-radius: 16px;
    overflow: hidden;
    box-shadow: 0 8px 24px rgba(0, 0, 0, 0.1);
    background: white;
}

.info-box {
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    border-radius: 12px;
    padding: 24px;
    border-left: 4px solid #667eea;
}

::-webkit-scrollbar {
    width: 8px;
}

::-webkit-scrollbar-track {
    background: #f1f1f1;
}

::-webkit-scrollbar-thumb {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    border-radius: 4px;
}

::-webkit-scrollbar-thumb:hover {
    background: #764ba2;
}

@keyframes fadeIn {
    from {
        opacity: 0;
        transform: translateY(20px);
    }
    to {
        opacity: 1;
        transform: translateY(0);
    }
}

.animate-in {
    animation: fadeIn 0.6s ease-out;
}
"""

with gr.Blocks() as demo:
    gr.HTML("""
        <div class="header-container animate-in">
            <h1>YOLOv12s Vehicle Detection</h1>
            <p>Detect vehicles in Egyptian streets with state-of-the-art AI</p>
        </div>
    """)
    
    with gr.Row(elem_classes="main-content"):
        with gr.Column(scale=1):
            gr.Markdown("### Upload Media")
            media = gr.File(
                label="Drop your image or video here",
                file_types=[".jpg", ".jpeg", ".png", ".mp4", ".avi"],
                elem_classes="file-upload-area"
            )
            
            gr.Markdown("### Media Type")
            media_type = gr.Radio(
                ["Image", "Video"],
                label="Select type",
                value="Image",
                elem_classes="radio-group"
            )
            
            btn = gr.Button("Run Detection", variant="primary", size="lg", elem_classes="primary")
            
            gr.Markdown("""
                ---
                ### Features
                - Real-time vehicle detection
                - Support for images and videos
                - Auto-logging to DagsHub
                - Detailed metrics tracking
            """)
        
        with gr.Column(scale=2):
            gr.Markdown("### Detection Results")
            
            with gr.Tabs():
                with gr.Tab("Image Results"):
                    with gr.Row():
                        img_original = gr.Image(
                            label="Original Image",
                            height=400,
                            elem_classes="output-image"
                        )
                        img_detected = gr.Image(
                            label="Detected Objects",
                            height=400,
                            elem_classes="output-image"
                        )
                
                with gr.Tab("Video Results"):
                    with gr.Row():
                        vid_original = gr.Video(
                            label="Original Video",
                            height=400,
                            elem_classes="output-video"
                        )
                        vid_detected = gr.Video(
                            label="Detected Objects",
                            height=400,
                            elem_classes="output-video"
                        )
            
            gr.Markdown("### Run Information")
            info = gr.Markdown(
                "Ready to detect... Upload a file and click Run Detection to start!",
                elem_classes="info-box"
            )
    
    btn.click(
        fn=run_inference,
        inputs=[media, media_type],
        outputs=[img_original, img_detected, vid_original, vid_detected, info]
    )
    
    gr.HTML("""
        <div style="text-align: center; padding: 32px; color: #6b7280;">
            <p>Powered by YOLOv12s - MLflow Tracking - DagsHub Integration</p>
        </div>
    """)

demo.launch(css=css, theme=gr.themes.Soft())