frontend/dist/assets/ComputerVision-TOZ6ptJE.js

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Please train a model first.");return}x(!0);try{await R.post(`/api/v1/cv/models/${m}/export`,{formats:p});const I=await R.get(`/api/v1/cv/export/${m}/download`,{responseType:"blob"}),y=window.URL.createObjectURL(new Blob([I.data])),D=document.createElement("a");D.href=y,D.setAttribute("download",`datavision_cv_model_${m}.zip`),document.body.appendChild(D),D.click(),(b=D.parentNode)==null||b.removeChild(D),r.success("Model ZIP package downloaded successfully!")}catch(I){r.error(((_=(N=I.response)==null?void 0:N.data)==null?void 0:_.detail)||"Download failed. Please try again.")}finally{x(!1)}},l=`import cv2
from ultralytics import YOLO

# Load the trained model weights
model = YOLO('models/best.pt')

# Run inference on any image
def predict(image_path):
    results = model(image_path)
    for r in results:
        # Print predictions
        if hasattr(r, 'probs') and r.probs is not None:
            top1_id = int(r.probs.top1)
            print(f"Prediction: {r.names[top1_id]} ({float(r.probs.top1conf)*100:.1f}%)")
        else:
            for box in r.boxes:
                print(f"Detected: {r.names[int(box.cls[0])]} ({float(box.conf[0]):.2f})")

if __name__ == '__main__':
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Upload your first dataset to get started."}):e.jsx("div",{className:"grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-3",children:p.slice(0,6).map(a=>e.jsxs("div",{onClick:()=>{M(a.id),u("data")},className:`p-4 rounded-xl border cursor-pointer transition-all hover:border-emerald-500/40 hover:-translate-y-0.5 ${S} ${t?"hover:bg-white/[0.02]":"hover:bg-slate-50"} ${x===a.id?"border-emerald-500/50 ring-1 ring-emerald-500/20":""}`,children:[e.jsx("div",{className:`font-bold text-sm mb-1.5 truncate ${l}`,children:a.name}),e.jsxs("div",{className:`text-xs flex gap-3 ${v}`,children:[e.jsxs("span",{className:"flex items-center gap-1",children:[e.jsx(J,{className:"w-3 h-3"})," ",a.numImages," imgs"]}),e.jsxs("span",{className:"flex items-center gap-1",children:[e.jsx(ae,{className:"w-3 h-3"})," ",a.numClasses," cls"]})]})]},a.id))})]}),g.length>0&&e.jsxs("div",{className:`p-6 rounded-2xl border ${d} ${S}`,children:[e.jsxs("div",{className:"flex items-center justify-between mb-4",children:[e.jsx("h3",{className:`text-sm font-bold ${l}`,children:"Training History"}),e.jsx("button",{onClick:()=>u("experiments"),className:"text-xs font-medium text-emerald-500 hover:text-emerald-400",children:"All Experiments →"})]}),e.jsx("div",{className:"space-y-2",children:g.slice(0,5).map(a=>{var n;return e.jsxs("div",{className:`flex items-center justify-between p-3 rounded-xl border ${S} ${t?"hover:bg-white/[0.02]":"hover:bg-slate-50"} transition-colors`,children:[e.jsxs("div",{className:"flex items-center gap-3",children:[e.jsx("div",{className:`w-2 h-2 rounded-full ${a.status==="completed"?"bg-emerald-500":a.status==="running"?"bg-blue-500 animate-pulse":a.status==="failed"?"bg-red-500":"bg-slate-400"}`}),e.jsxs("div",{children:[e.jsx("span",{className:`text-xs font-bold ${l}`,children:a.config.model.toUpperCase()}),e.jsxs("span",{className:`text-xs ${s} ml-2`,children:[a.mode," · ",a.progress.totalEpochs," epochs"]})]})]}),e.jsxs("div",{className:"flex items-center gap-4",children:[((n=a.metrics)==null?void 0:n.mAP50)&&e.jsxs("span",{className:"text-xs font-bold text-emerald-500 font-mono",children:["mAP: ",(a.metrics.mAP50*100).toFixed(1),"%"]}),e.jsx("span",{className:`text-[10px] font-bold px-2 py-0.5 rounded-full uppercase tracking-wider ${a.status==="completed"?"bg-emerald-500/10 text-emerald-500":a.status==="running"?"bg-blue-500/10 text-blue-500":a.status==="failed"?"bg-red-500/10 text-red-500":"bg-slate-500/10 text-slate-500"}`,children:a.status})]})]},a.id)})})]})]}),h==="data"&&e.jsxs("div",{className:"space-y-6",children:[e.jsx(cs,{onUploadComplete:_}),e.jsx(ms,{})]}),h==="vision_tasks"&&e.jsxs("div",{className:"space-y-6",children:[e.jsxs("div",{className:"flex items-center gap-3 mb-2",children:[e.jsx("div",{className:"p-2 rounded-xl bg-emerald-500/10",children:e.jsx(ze,{className:"w-5 h-5 text-emerald-500"})}),e.jsxs("div",{children:[e.jsx("h2",{className:`text-lg font-bold ${l}`,children:"Vision Tasks"}),e.jsx("p",{className:`text-xs ${v}`,children:"Auto-detected from your dataset. 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