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David Ko commited on 29 days ago

Commit

96aa590

1 Parent(s): ea52b48

fix(frontend): force preview <img> to contain within container; rebuild & sync CRA assets

Browse files

Files changed (9) hide show

frontend/src/components/ImageUploader.js +7 -0
static/asset-manifest.json +4 -4
static/index.html +1 -1
static/{precache-manifest.7ffa256e4ee99cb22c2ef5bb2f02bd1a.js → precache-manifest.4683f6cd857bd9aa85304ac13a82d061.js} +4 -4
static/service-worker.js +1 -1
static/static/js/main.13ed4cd9.chunk.js +0 -2
static/static/js/main.13ed4cd9.chunk.js.map +0 -1
static/static/js/main.fc0e2dc7.chunk.js +2 -0
static/static/js/main.fc0e2dc7.chunk.js.map +1 -0

frontend/src/components/ImageUploader.js CHANGED Viewed

@@ -179,6 +179,13 @@ const ImageUploader = ({ onImageUpload }) => {
               src={previewUrl}
               alt="Preview"
               className="preview-image"
             />
             <IconButton
               aria-label="delete"

               src={previewUrl}
               alt="Preview"
               className="preview-image"
+              style={{
+                width: '100%',
+                height: 'auto',
+                maxHeight: '100%',
+                objectFit: 'contain',
+                display: 'block'
+              }}
             />
             <IconButton
               aria-label="delete"

static/asset-manifest.json CHANGED Viewed

@@ -1,8 +1,8 @@
 {
   "files": {
     "main.css": "/static/css/main.3b4eede1.chunk.css",
-    "main.js": "/static/js/main.13ed4cd9.chunk.js",
-    "main.js.map": "/static/js/main.13ed4cd9.chunk.js.map",
     "runtime-main.js": "/static/js/runtime-main.25710301.js",
     "runtime-main.js.map": "/static/js/runtime-main.25710301.js.map",
     "static/js/2.9b5b80e8.chunk.js": "/static/js/2.9b5b80e8.chunk.js",
@@ -10,7 +10,7 @@
     "static/js/3.9013e23f.chunk.js": "/static/js/3.9013e23f.chunk.js",
     "static/js/3.9013e23f.chunk.js.map": "/static/js/3.9013e23f.chunk.js.map",
     "index.html": "/index.html",
-    "precache-manifest.7ffa256e4ee99cb22c2ef5bb2f02bd1a.js": "/precache-manifest.7ffa256e4ee99cb22c2ef5bb2f02bd1a.js",
     "service-worker.js": "/service-worker.js",
     "static/css/main.3b4eede1.chunk.css.map": "/static/css/main.3b4eede1.chunk.css.map",
     "static/js/2.9b5b80e8.chunk.js.LICENSE.txt": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
@@ -19,6 +19,6 @@
     "static/js/runtime-main.25710301.js",
     "static/js/2.9b5b80e8.chunk.js",
     "static/css/main.3b4eede1.chunk.css",
-    "static/js/main.13ed4cd9.chunk.js"
   ]
 }

 {
   "files": {
     "main.css": "/static/css/main.3b4eede1.chunk.css",
+    "main.js": "/static/js/main.fc0e2dc7.chunk.js",
+    "main.js.map": "/static/js/main.fc0e2dc7.chunk.js.map",
     "runtime-main.js": "/static/js/runtime-main.25710301.js",
     "runtime-main.js.map": "/static/js/runtime-main.25710301.js.map",
     "static/js/2.9b5b80e8.chunk.js": "/static/js/2.9b5b80e8.chunk.js",
     "static/js/3.9013e23f.chunk.js": "/static/js/3.9013e23f.chunk.js",
     "static/js/3.9013e23f.chunk.js.map": "/static/js/3.9013e23f.chunk.js.map",
     "index.html": "/index.html",
+    "precache-manifest.4683f6cd857bd9aa85304ac13a82d061.js": "/precache-manifest.4683f6cd857bd9aa85304ac13a82d061.js",
     "service-worker.js": "/service-worker.js",
     "static/css/main.3b4eede1.chunk.css.map": "/static/css/main.3b4eede1.chunk.css.map",
     "static/js/2.9b5b80e8.chunk.js.LICENSE.txt": "/static/js/2.9b5b80e8.chunk.js.LICENSE.txt"
     "static/js/runtime-main.25710301.js",
     "static/js/2.9b5b80e8.chunk.js",
     "static/css/main.3b4eede1.chunk.css",
+    "static/js/main.fc0e2dc7.chunk.js"
   ]
 }

static/index.html CHANGED Viewed

@@ -1 +1 @@

- <!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.3b4eede1.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],p=0,s=[];p<a.length;p++)i=a[p],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(f&&f(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"9013e23f"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var f=c;t()}([])</script><script src="/static/js/2.9b5b80e8.chunk.js"></script><script src="/static/js/main.~~13ed4cd9~~.chunk.js"></script></body></html>

+ <!doctype html><html lang="en"><head><meta charset="utf-8"/><link rel="icon" href="/favicon.ico"/><meta name="viewport" content="width=device-width,initial-scale=1"/><meta name="theme-color" content="#000000"/><meta name="description" content="Multi-Model Object Detection Demo"/><link rel="apple-touch-icon" href="/logo192.png"/><link rel="manifest" href="/manifest.json"/><title>Vision Web App</title><link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"/><link href="/static/css/main.3b4eede1.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,i,a=r[0],c=r[1],l=r[2],p=0,s=[];p<a.length;p++)i=a[p],Object.prototype.hasOwnProperty.call(o,i)&&o[i]&&s.push(o[i][0]),o[i]=0;for(n in c)Object.prototype.hasOwnProperty.call(c,n)&&(e[n]=c[n]);for(f&&f(r);s.length;)s.shift()();return u.push.apply(u,l||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var c=t[a];0!==o[c]&&(n=!1)}n&&(u.splice(r--,1),e=i(i.s=t[0]))}return e}var n={},o={1:0},u=[];function i(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,i),t.l=!0,t.exports}i.e=function(e){var r=[],t=o[e];if(0!==t)if(t)r.push(t[2]);else{var n=new Promise((function(r,n){t=o[e]=[r,n]}));r.push(t[2]=n);var u,a=document.createElement("script");a.charset="utf-8",a.timeout=120,i.nc&&a.setAttribute("nonce",i.nc),a.src=function(e){return i.p+"static/js/"+({}[e]||e)+"."+{3:"9013e23f"}[e]+".chunk.js"}(e);var c=new Error;u=function(r){a.onerror=a.onload=null,clearTimeout(l);var t=o[e];if(0!==t){if(t){var n=r&&("load"===r.type?"missing":r.type),u=r&&r.target&&r.target.src;c.message="Loading chunk "+e+" failed.\n("+n+": "+u+")",c.name="ChunkLoadError",c.type=n,c.request=u,t[1](c)}o[e]=void 0}};var l=setTimeout((function(){u({type:"timeout",target:a})}),12e4);a.onerror=a.onload=u,document.head.appendChild(a)}return Promise.all(r)},i.m=e,i.c=n,i.d=function(e,r,t){i.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},i.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.t=function(e,r){if(1&r&&(e=i(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(i.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)i.d(t,n,function(r){return e[r]}.bind(null,n));return t},i.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return i.d(r,"a",r),r},i.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},i.p="/",i.oe=function(e){throw console.error(e),e};var a=this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]||[],c=a.push.bind(a);a.push=r,a=a.slice();for(var l=0;l<a.length;l++)r(a[l]);var f=c;t()}([])</script><script src="/static/js/2.9b5b80e8.chunk.js"></script><script src="/static/js/main.fc0e2dc7.chunk.js"></script></body></html>

static/{precache-manifest.7ffa256e4ee99cb22c2ef5bb2f02bd1a.js → precache-manifest.4683f6cd857bd9aa85304ac13a82d061.js} RENAMED Viewed

@@ -1,10 +1,10 @@
 self.__precacheManifest = (self.__precacheManifest || []).concat([
   {
-    "revision": "0b39b3518ff769f69904f679df2e185f",
     "url": "/index.html"
   },
   {
-    "revision": "d64110ad919024c5c0ac",
     "url": "/static/css/main.3b4eede1.chunk.css"
   },
   {
@@ -20,8 +20,8 @@ self.__precacheManifest = (self.__precacheManifest || []).concat([
     "url": "/static/js/3.9013e23f.chunk.js"
   },
   {
-    "revision": "d64110ad919024c5c0ac",
-    "url": "/static/js/main.13ed4cd9.chunk.js"
   },
   {
     "revision": "d8c310b0ac7ffa6d8151",

 self.__precacheManifest = (self.__precacheManifest || []).concat([
   {
+    "revision": "d54f7d4646b42d5859a3c60dc17c2cbc",
     "url": "/index.html"
   },
   {
+    "revision": "bb5a62e98165b2cca977",
     "url": "/static/css/main.3b4eede1.chunk.css"
   },
   {
     "url": "/static/js/3.9013e23f.chunk.js"
   },
   {
+    "revision": "bb5a62e98165b2cca977",
+    "url": "/static/js/main.fc0e2dc7.chunk.js"
   },
   {
     "revision": "d8c310b0ac7ffa6d8151",

static/service-worker.js CHANGED Viewed

@@ -14,7 +14,7 @@
 importScripts("https://storage.googleapis.com/workbox-cdn/releases/4.3.1/workbox-sw.js");
 importScripts(
-  "/precache-manifest.7ffa256e4ee99cb22c2ef5bb2f02bd1a.js"
 );
 self.addEventListener('message', (event) => {

 importScripts("https://storage.googleapis.com/workbox-cdn/releases/4.3.1/workbox-sw.js");
 importScripts(
+  "/precache-manifest.4683f6cd857bd9aa85304ac13a82d061.js"
 );
 self.addEventListener('message', (event) => {

static/static/js/main.13ed4cd9.chunk.js DELETED Viewed

	@@ -1,2 +0,0 @@
1	- (this["webpackJsonpvision-web-app"]=this["webpackJsonpvision-web-app"]\|\|[]).push([[0],{102:function(e,a,t){},103:function(e,a,t){"use strict";t.r(a);var n=t(0),r=t.n(n),l=t(11),o=t.n(l),c=(t(97),t(78)),i=t(155),s=t(159),m=t(156),d=t(157),u=t(48),g=t(158),p=t(138),E=t(81),h=t(143),y=t(136),b=t(137),f=t(63),v=t.n(f),x=t(75),S=t.n(x),C=t(133);const j=Object(C.a)(e=>({paper:{padding:e.spacing(2),display:"flex",flexDirection:"column",alignItems:"center",height:"100%",minHeight:300,transition:"all 0.3s ease"},dragActive:{border:"2px dashed #3f51b5",backgroundColor:"rgba(63, 81, 181, 0.05)"},dragInactive:{border:"2px dashed #ccc",backgroundColor:"white"},uploadBox:{display:"flex",flexDirection:"column",alignItems:"center",justifyContent:"center",height:"100%",width:"100%",cursor:"pointer"},uploadIcon:{fontSize:60,color:"#3f51b5",marginBottom:e.spacing(2)},supportText:{marginTop:e.spacing(2)},previewBox:{display:"flex",flexDirection:"column",alignItems:"center",width:"100%",height:"100%",position:"relative"},imageContainer:{position:"relative",width:"100%",height:"60vh",[e.breakpoints.down("sm")]:{height:"45vh"},display:"flex",justifyContent:"center",alignItems:"center",overflow:"hidden",marginTop:e.spacing(2)},deleteButton:{position:"absolute",top:0,right:0,backgroundColor:"rgba(255, 255, 255, 0.7)","&:hover":{backgroundColor:"rgba(255, 255, 255, 0.9)"}}}));var w=e=>{let{onImageUpload:a}=e;const t=j(),[l,o]=Object(n.useState)(null),[c,i]=Object(n.useState)(!1),m=Object(n.useRef)(null),d=e=>{e.preventDefault(),e.stopPropagation(),"dragenter"===e.type\|\|"dragover"===e.type?i(!0):"dragleave"===e.type&&i(!1)},g=e=>{e.type.startsWith("image/")?(o(URL.createObjectURL(e)),a(e)):alert("Please upload an image file")};return r.a.createElement(E.a,{className:"".concat(t.paper," ").concat(c?t.dragActive:t.dragInactive),onDragEnter:d,onDragLeave:d,onDragOver:d,onDrop:e=>{e.preventDefault(),e.stopPropagation(),i(!1),e.dataTransfer.files&&e.dataTransfer.files[0]&&g(e.dataTransfer.files[0])}},r.a.createElement("input",{ref:m,type:"file",accept:"image/",onChange:e=>{e.preventDefault(),e.target.files&&e.target.files[0]&&g(e.target.files[0])},style:{display:"none"}}),l?r.a.createElement(s.a,{className:t.previewBox},r.a.createElement(u.a,{variant:"h6",gutterBottom:!0},"Preview"),r.a.createElement(s.a,{className:t.imageContainer},r.a.createElement("img",{src:l,alt:"Preview",className:"preview-image"}),r.a.createElement(b.a,{"aria-label":"delete",className:t.deleteButton,onClick:()=>{o(null),a(null),m.current.value=""}},r.a.createElement(S.a,null)))):r.a.createElement(s.a,{className:t.uploadBox,onClick:()=>{m.current.click()}},r.a.createElement(v.a,{className:t.uploadIcon}),r.a.createElement(u.a,{variant:"h6",gutterBottom:!0},"Drag & Drop an image 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i=R(),m=[{id:"yolo",name:"YOLOv8",description:"Fast and accurate object detection",icon:r.a.createElement(k.a,null),available:l.yolo},{id:"detr",name:"DETR",description:"DEtection TRansformer for object detection",icon:r.a.createElement(k.a,null),available:l.detr},{id:"vit",name:"ViT",description:"Vision Transformer for image classification",icon:r.a.createElement(D.a,null),available:l.vit}],d=e=>{m.find(a=>a.id===e).available&&a(e)};return r.a.createElement(s.a,{sx:{p:2,height:"100%"}},r.a.createElement(u.a,{variant:"h6",gutterBottom:!0},"Select Model"),r.a.createElement(p.a,{container:!0,spacing:2},m.map(e=>r.a.createElement(p.a,{item:!0,xs:12,sm:4,key:e.id},r.a.createElement(O.a,{className:"\n ".concat(i.card," \n ").concat(o===e.id?i.selectedCard:""," \n ").concat(e.available?"":i.unavailableCard,"\n 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Image")))},L=t(153),M=t(150),W=t(105),_=t(154),J=t(142),U=t(164),H=t(163),V=t(145),G=t(146),Y=t(147),q=t(167),K=t(160),Q=t(151),X=t(169),Z=t(152),$=t(161);const ee=Object(C.a)(e=>({root:{marginTop:e.spacing(2),marginBottom:e.spacing(2),padding:e.spacing(2),backgroundColor:"#f5f5f5",borderRadius:e.shape.borderRadius},button:{marginRight:e.spacing(2)},searchDialog:{minWidth:"500px"},formControl:{marginBottom:e.spacing(2),minWidth:"100%"},searchResults:{marginTop:e.spacing(2)},resultCard:{marginBottom:e.spacing(2)},resultImage:{height:140,objectFit:"contain"},chip:{margin:e.spacing(.5)},similarityChip:{backgroundColor:e.palette.primary.main,color:"white"}}));var ae=e=>{let{results:a}=e;const 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React, { useState, useRef } from 'react';\nimport { \n Paper, \n Typography, \n Box, \n Button, \n IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n paper: {\n padding: theme.spacing(2),\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n height: '100%',\n minHeight: 300,\n transition: 'all 0.3s ease'\n },\n dragActive: {\n border: '2px dashed #3f51b5',\n backgroundColor: 'rgba(63, 81, 181, 0.05)'\n },\n dragInactive: {\n border: '2px dashed #ccc',\n backgroundColor: 'white'\n },\n uploadBox: {\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n justifyContent: 'center',\n height: '100%',\n width: '100%',\n cursor: 'pointer'\n },\n uploadIcon: {\n fontSize: 60,\n color: '#3f51b5',\n marginBottom: theme.spacing(2)\n },\n supportText: {\n marginTop: theme.spacing(2)\n },\n previewBox: {\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n width: '100%',\n height: '100%',\n position: 'relative'\n },\n imageContainer: {\n position: 'relative',\n width: '100%',\n // Use viewport-based height so any aspect ratio fits inside\n height: '60vh',\n [theme.breakpoints.down('sm')]: {\n height: '45vh',\n },\n display: 'flex',\n justifyContent: 'center',\n alignItems: 'center',\n overflow: 'hidden',\n marginTop: theme.spacing(2),\n },\n deleteButton: {\n position: 'absolute',\n top: 0,\n right: 0,\n backgroundColor: 'rgba(255, 255, 255, 0.7)',\n '&:hover': {\n backgroundColor: 'rgba(255, 255, 255, 0.9)',\n }\n }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n const classes = useStyles();\n const [previewUrl, setPreviewUrl] = useState(null);\n const [dragActive, setDragActive] = useState(false);\n const fileInputRef = useRef(null);\n\n const handleDrag = (e) => {\n e.preventDefault();\n e.stopPropagation();\n if (e.type === 'dragenter' || e.type === 'dragover') {\n setDragActive(true);\n } else if (e.type === 'dragleave') {\n setDragActive(false);\n }\n };\n\n const handleDrop = (e) => {\n e.preventDefault();\n e.stopPropagation();\n setDragActive(false);\n if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n handleFiles(e.dataTransfer.files[0]);\n }\n };\n\n const handleChange = (e) => {\n e.preventDefault();\n if (e.target.files && e.target.files[0]) {\n handleFiles(e.target.files[0]);\n }\n };\n\n const handleFiles = (file) => {\n if (file.type.startsWith('image/')) {\n setPreviewUrl(URL.createObjectURL(file));\n onImageUpload(file);\n } else {\n alert('Please upload an image file');\n }\n };\n\n const onButtonClick = () => {\n fileInputRef.current.click();\n };\n\n const handleRemoveImage = () => {\n setPreviewUrl(null);\n onImageUpload(null);\n fileInputRef.current.value = \"\";\n };\n\n return (\n <Paper \n className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n onDragEnter={handleDrag}\n onDragLeave={handleDrag}\n onDragOver={handleDrag}\n onDrop={handleDrop}\n >\n <input\n ref={fileInputRef}\n type=\"file\"\n accept=\"image/*\"\n onChange={handleChange}\n style={{ display: 'none' }}\n />\n\n {!previewUrl ? (\n <Box \n className={classes.uploadBox}\n onClick={onButtonClick}\n >\n <CloudUploadIcon className={classes.uploadIcon} />\n <Typography variant=\"h6\" gutterBottom>\n Drag & Drop an image here\n </Typography>\n <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n or\n </Typography>\n <Button\n variant=\"contained\"\n color=\"primary\"\n component=\"span\"\n startIcon={<CloudUploadIcon />}\n >\n Browse Files\n </Button>\n <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n Supported formats: JPG, PNG, GIF\n </Typography>\n </Box>\n ) : (\n <Box className={classes.previewBox}>\n <Typography variant=\"h6\" gutterBottom>\n Preview\n </Typography>\n <Box className={classes.imageContainer}>\n <img\n src={previewUrl}\n alt=\"Preview\"\n className=\"preview-image\"\n />\n <IconButton\n aria-label=\"delete\"\n className={classes.deleteButton}\n onClick={handleRemoveImage}\n >\n <DeleteIcon />\n </IconButton>\n </Box>\n </Box>\n )}\n </Paper>\n );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n Grid, \n Card, \n CardContent, \n CardActions, \n Typography, \n Button, \n Chip,\n Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n card: {\n height: '100%',\n display: 'flex',\n flexDirection: 'column',\n },\n selectedCard: {\n border: '2px solid #3f51b5',\n },\n unavailableCard: {\n opacity: 0.6,\n },\n cardContent: {\n flexGrow: 1,\n },\n chipContainer: {\n marginBottom: theme.spacing(1.5),\n },\n successChip: {\n backgroundColor: '#34C759',\n color: '#fff',\n },\n errorChip: {\n backgroundColor: '#FF3B3F',\n color: '#fff',\n },\n modelType: {\n marginTop: theme.spacing(1),\n },\n processButton: {\n marginTop: theme.spacing(3),\n textAlign: 'center',\n }\n}));\n\nconst ModelSelector = ({ \n onModelSelect, \n onProcess, \n isProcessing, \n modelsStatus, \n selectedModel,\n imageSelected \n}) => {\n const classes = useStyles();\n \n const models = [\n {\n id: 'yolo',\n name: 'YOLOv8',\n description: 'Fast and accurate object detection',\n icon: <VisibilityIcon />,\n available: modelsStatus.yolo\n },\n {\n id: 'detr',\n name: 'DETR',\n description: 'DEtection TRansformer for object detection',\n icon: <VisibilityIcon />,\n available: modelsStatus.detr\n },\n {\n id: 'vit',\n name: 'ViT',\n description: 'Vision Transformer for image classification',\n icon: <CategoryIcon />,\n available: modelsStatus.vit\n }\n ];\n\n const handleModelClick = (modelId) => {\n if (models.find(m => m.id === modelId).available) {\n onModelSelect(modelId);\n }\n };\n\n return (\n <Box sx={{ p: 2, height: '100%' }}>\n <Typography variant=\"h6\" gutterBottom>\n Select Model\n </Typography>\n \n <Grid container spacing={2}>\n {models.map((model) => (\n <Grid item xs={12} sm={4} key={model.id}>\n <Card \n className={`\n ${classes.card} \n ${selectedModel === model.id ? classes.selectedCard : ''} \n ${!model.available ? classes.unavailableCard : ''}\n `}\n onClick={() => handleModelClick(model.id)}\n >\n <CardContent className={classes.cardContent}>\n <Box sx={{ mb: 2, color: 'primary' }}>\n {model.icon}\n </Box>\n <Typography variant=\"h5\" component=\"div\" gutterBottom>\n {model.name}\n </Typography>\n <div className={classes.chipContainer}>\n {model.available ? (\n <Chip \n label=\"Available\" \n className={classes.successChip}\n size=\"small\" \n />\n ) : (\n <Chip \n label=\"Not Available\" \n className={classes.errorChip}\n size=\"small\" \n />\n )}\n </div>\n <Typography variant=\"body2\" color=\"textSecondary\">\n {model.description}\n </Typography>\n </CardContent>\n <CardActions>\n <Button \n size=\"small\" \n onClick={() => handleModelClick(model.id)}\n disabled={!model.available}\n color={selectedModel === model.id ? \"primary\" : \"default\"}\n variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n fullWidth\n >\n {selectedModel === model.id ? 'Selected' : 'Select'}\n </Button>\n </CardActions>\n </Card>\n </Grid>\n ))}\n </Grid>\n\n <div className={classes.processButton}>\n <Button\n variant=\"contained\"\n color=\"primary\"\n size=\"large\"\n startIcon={<PlayArrowIcon />}\n onClick={onProcess}\n disabled={!selectedModel || !imageSelected || isProcessing}\n >\n {isProcessing ? 'Processing...' : 'Process Image'}\n </Button>\n </div>\n </Box>\n );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n Button, \n Box, \n Typography, \n CircularProgress, \n Snackbar,\n Dialog,\n DialogTitle,\n DialogContent,\n DialogActions,\n TextField,\n FormControl,\n InputLabel,\n Select,\n MenuItem,\n Grid,\n Card,\n CardMedia,\n CardContent,\n Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n root: {\n marginTop: theme.spacing(2),\n marginBottom: theme.spacing(2),\n padding: theme.spacing(2),\n backgroundColor: '#f5f5f5',\n borderRadius: theme.shape.borderRadius,\n },\n button: {\n marginRight: theme.spacing(2),\n },\n searchDialog: {\n minWidth: '500px',\n },\n formControl: {\n marginBottom: theme.spacing(2),\n minWidth: '100%',\n },\n searchResults: {\n marginTop: theme.spacing(2),\n },\n resultCard: {\n marginBottom: theme.spacing(2),\n },\n resultImage: {\n height: 140,\n objectFit: 'contain',\n },\n chip: {\n margin: theme.spacing(0.5),\n },\n similarityChip: {\n backgroundColor: theme.palette.primary.main,\n color: 'white',\n }\n}));\n\nconst VectorDBActions = ({ results }) => {\n const classes = useStyles();\n const [isSaving, setIsSaving] = useState(false);\n const [saveSuccess, setSaveSuccess] = useState(false);\n const [saveError, setSaveError] = useState(null);\n const [openSearchDialog, setOpenSearchDialog] = useState(false);\n const [searchType, setSearchType] = useState('image');\n const [searchClass, setSearchClass] = useState('');\n const [searchResults, setSearchResults] = useState([]);\n const [isSearching, setIsSearching] = useState(false);\n const [searchError, setSearchError] = useState(null);\n \n // Extract model and data from results\n const { model, data } = results;\n \n // Handle saving to vector DB\n const handleSaveToVectorDB = async () => {\n setIsSaving(true);\n setSaveError(null);\n \n try {\n let response;\n \n if (model === 'vit') {\n // For ViT, save the whole image with classifications\n response = await fetch('/api/add-to-collection', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n image: data.image,\n metadata: {\n model: 'vit',\n classifications: data.classifications\n }\n })\n });\n } else {\n // For YOLO and DETR, save detected objects\n response = await fetch('/api/add-detected-objects', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n image: data.image,\n objects: data.detections,\n imageId: generateUUID()\n })\n });\n }\n \n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n \n const result = await response.json();\n \n if (result.error) {\n throw new Error(result.error);\n }\n \n setSaveSuccess(true);\n setTimeout(() => setSaveSuccess(false), 5000);\n } catch (err) {\n console.error('Error saving to vector DB:', err);\n setSaveError(`Error saving to vector DB: ${err.message}`);\n } finally {\n setIsSaving(false);\n }\n };\n \n // Handle opening search dialog\n const handleOpenSearchDialog = () => {\n setOpenSearchDialog(true);\n setSearchResults([]);\n setSearchError(null);\n };\n \n // Handle closing search dialog\n const handleCloseSearchDialog = () => {\n setOpenSearchDialog(false);\n };\n \n // Handle search type change\n const handleSearchTypeChange = (event) => {\n setSearchType(event.target.value);\n setSearchResults([]);\n setSearchError(null);\n };\n \n // Handle search class change\n const handleSearchClassChange = (event) => {\n setSearchClass(event.target.value);\n };\n \n // Handle search\n const handleSearch = async () => {\n setIsSearching(true);\n setSearchError(null);\n \n try {\n let requestBody = {};\n \n if (searchType === 'image') {\n // Search by current image\n requestBody = {\n searchType: 'image',\n image: data.image,\n n_results: 5\n };\n } else {\n // Search by class name\n if (!searchClass.trim()) {\n throw new Error('Please enter a class name');\n }\n \n requestBody = {\n searchType: 'class',\n class_name: searchClass.trim(),\n n_results: 5\n };\n }\n \n const response = await fetch('/api/search-similar-objects', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(requestBody)\n });\n \n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n \n const result = await response.json();\n \n if (result.error) {\n throw new Error(result.error);\n }\n \n console.log('Search API response:', result);\n \n // The backend responds with {success, searchType, results} structure, so extract only the results array\n if (result.success && Array.isArray(result.results)) {\n console.log('Setting search results array:', result.results);\n console.log('Results array length:', result.results.length);\n console.log('First result item:', result.results[0]);\n setSearchResults(result.results);\n } else {\n console.error('Unexpected API response format:', result);\n throw new Error('Unexpected API response format');\n }\n } catch (err) {\n console.error('Error searching vector DB:', err);\n setSearchError(`Error searching vector DB: ${err.message}`);\n } finally {\n setIsSearching(false);\n }\n };\n \n // Generate UUID for image ID\n const generateUUID = () => {\n return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n const r = Math.random() * 16 | 0;\n const v = c === 'x' ? r : (r & 0x3 | 0x8);\n return v.toString(16);\n });\n };\n \n // Render search results\n const renderSearchResults = () => {\n console.log('Rendering search results:', searchResults);\n console.log('Search results length:', searchResults.length);\n \n if (searchResults.length === 0) {\n console.log('No results to render');\n return (\n <Typography variant=\"body1\">No results found.</Typography>\n );\n }\n \n return (\n <Grid container spacing={2}>\n {searchResults.map((result, index) => {\n const similarity = (1 - result.distance) * 100;\n \n return (\n <Grid item xs={12} sm={6} key={index}>\n <Card className={classes.resultCard}>\n {result.metadata && result.metadata.image_data ? (\n <CardMedia\n className={classes.resultImage}\n component=\"img\"\n height=\"200\"\n image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n />\n ) : (\n <Box \n className={classes.resultImage}\n style={{ \n backgroundColor: '#f0f0f0', \n display: 'flex', \n alignItems: 'center', \n justifyContent: 'center',\n height: 200\n }}\n >\n <Typography variant=\"body2\" color=\"textSecondary\">\n {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n </Typography>\n </Box>\n )}\n <CardContent>\n <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n <Chip \n label={`Similarity: ${similarity.toFixed(2)}%`}\n className={classes.similarityChip}\n size=\"small\"\n />\n </Box>\n <Typography variant=\"body2\" color=\"textSecondary\">\n <strong>Class:</strong> {result.metadata.class || 'N/A'}\n </Typography>\n {result.metadata.confidence && (\n <Typography variant=\"body2\" color=\"textSecondary\">\n <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n </Typography>\n )}\n <Typography variant=\"body2\" color=\"textSecondary\">\n <strong>Object ID:</strong> {result.id}\n </Typography>\n </CardContent>\n </Card>\n </Grid>\n );\n })}\n </Grid>\n );\n };\n \n return (\n <Box className={classes.root}>\n <Typography variant=\"h6\" gutterBottom>\n Vector Database Actions\n </Typography>\n \n <Box display=\"flex\" alignItems=\"center\" mb={2}>\n <Button\n variant=\"contained\"\n color=\"primary\"\n onClick={handleSaveToVectorDB}\n disabled={isSaving}\n className={classes.button}\n >\n {isSaving ? (\n <>\n <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n Saving...\n </>\n ) : (\n 'Save to Vector DB'\n )}\n </Button>\n \n <Button\n variant=\"outlined\"\n color=\"primary\"\n onClick={handleOpenSearchDialog}\n className={classes.button}\n >\n Search Similar\n </Button>\n </Box>\n \n {saveError && (\n <Alert severity=\"error\" style={{ marginTop: 8 }}>\n {saveError}\n </Alert>\n )}\n \n <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n <Alert severity=\"success\">\n {model === 'vit' ? (\n 'Image and classifications successfully saved to vector DB!'\n ) : (\n 'Detected objects successfully saved to vector DB!'\n )}\n </Alert>\n </Snackbar>\n \n {/* Search Dialog */}\n <Dialog\n open={openSearchDialog}\n onClose={handleCloseSearchDialog}\n maxWidth=\"md\"\n fullWidth\n >\n <DialogTitle>Search Vector Database</DialogTitle>\n <DialogContent>\n <FormControl className={classes.formControl}>\n <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n <Select\n labelId=\"search-type-label\"\n id=\"search-type\"\n value={searchType}\n onChange={handleSearchTypeChange}\n >\n <MenuItem value=\"image\">Search by Current Image</MenuItem>\n <MenuItem value=\"class\">Search by Class Name</MenuItem>\n </Select>\n </FormControl>\n \n {searchType === 'class' && (\n <FormControl className={classes.formControl}>\n <TextField\n label=\"Class Name\"\n value={searchClass}\n onChange={handleSearchClassChange}\n placeholder=\"e.g. person, car, dog...\"\n fullWidth\n />\n </FormControl>\n )}\n \n {searchError && (\n <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n {searchError}\n </Alert>\n )}\n \n <Box className={classes.searchResults}>\n {isSearching ? (\n <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n <CircularProgress />\n <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n Searching...\n </Typography>\n </Box>\n ) : (\n <>\n {console.log('Search dialog render - searchResults:', searchResults)}\n {searchResults.length > 0 ? renderSearchResults() : \n <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n }\n </>\n )}\n </Box>\n </DialogContent>\n <DialogActions>\n <Button onClick={handleCloseSearchDialog} color=\"default\">\n Close\n </Button>\n <Button \n onClick={handleSearch} \n color=\"primary\" \n variant=\"contained\"\n disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n >\n Search\n </Button>\n </DialogActions>\n </Dialog>\n </Box>\n );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n Paper, \n Typography, \n Box, \n List, \n ListItem, \n ListItemText, \n Divider,\n Grid,\n Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n paper: {\n padding: theme.spacing(2)\n },\n marginBottom: {\n marginBottom: theme.spacing(2)\n },\n resultImage: {\n maxWidth: '100%',\n maxHeight: '400px',\n objectFit: 'contain'\n },\n dividerMargin: {\n margin: `${theme.spacing(2)}px 0`\n },\n chipContainer: {\n display: 'flex',\n gap: theme.spacing(1),\n flexWrap: 'wrap'\n }\n}));\n\nconst ResultDisplay = ({ results }) => {\n const classes = useStyles();\n if (!results) return null;\n \n const { model, data } = results;\n \n // Helper to format times nicely\n const formatTime = (ms) => {\n if (ms === undefined || ms === null || isNaN(ms)) return '-';\n const num = Number(ms);\n if (num < 1000) return `${num.toFixed(2)} ms`;\n return `${(num / 1000).toFixed(2)} s`;\n };\n \n // Check if there's an error\n if (data.error) {\n return (\n <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n <Typography color=\"error\">{data.error}</Typography>\n </Paper>\n );\n }\n\n // Display performance info\n const renderPerformanceInfo = () => {\n if (!data.performance) return null;\n \n return (\n <Box className=\"performance-info\">\n <Divider className={classes.dividerMargin} />\n <Typography variant=\"body2\">\n Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n </Typography>\n </Box>\n );\n };\n\n // Render for YOLO and DETR (object detection)\n if (model === 'yolo' || model === 'detr') {\n return (\n <Paper className={classes.paper}>\n <Typography variant=\"h6\" gutterBottom>\n {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n </Typography>\n \n <Grid container spacing={3}>\n <Grid item xs={12} md={6}>\n {data.image && (\n <Box className={classes.marginBottom}>\n <Typography variant=\"subtitle1\" gutterBottom>\n Detection Result\n </Typography>\n <img \n src={`data:image/png;base64,${data.image}`} \n alt=\"Detection Result\" \n className={classes.resultImage}\n />\n </Box>\n )}\n </Grid>\n \n <Grid item xs={12} md={6}>\n <Box className={classes.marginBottom}>\n <Typography variant=\"subtitle1\" gutterBottom>\n Detected Objects:\n </Typography>\n \n {data.detections && data.detections.length > 0 ? (\n <List>\n {data.detections.map((detection, index) => (\n <React.Fragment key={index}>\n <ListItem>\n <ListItemText \n primary={\n <Box style={{ display: 'flex', alignItems: 'center' }}>\n <Typography variant=\"body1\" component=\"span\">\n {detection.class}\n </Typography>\n <Chip \n label={`${(detection.confidence * 100).toFixed(0)}%`}\n size=\"small\"\n color=\"primary\"\n style={{ marginLeft: 8 }}\n />\n </Box>\n } \n secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n />\n </ListItem>\n {index < data.detections.length - 1 && <Divider />}\n </React.Fragment>\n ))}\n </List>\n ) : (\n <Typography variant=\"body1\">No objects detected</Typography>\n )}\n </Box>\n </Grid>\n </Grid>\n \n {renderPerformanceInfo()}\n \n {/* Vector DB Actions for Object Detection */}\n <VectorDBActions results={results} />\n </Paper>\n );\n }\n \n // Render for ViT (classification)\n if (model === 'vit') {\n return (\n <Paper className={classes.paper}>\n <Typography variant=\"h6\" gutterBottom>\n ViT Classification Results\n </Typography>\n \n <Typography variant=\"subtitle1\" gutterBottom>\n Top Predictions:\n </Typography>\n \n {data.top_predictions && data.top_predictions.length > 0 ? (\n <List>\n {data.top_predictions.map((prediction, index) => (\n <React.Fragment key={index}>\n <ListItem>\n <ListItemText \n primary={\n <Box style={{ display: 'flex', alignItems: 'center' }}>\n <Typography variant=\"body1\" component=\"span\">\n {prediction.rank}. {prediction.class}\n </Typography>\n <Chip \n label={`${(prediction.probability * 100).toFixed(1)}%`}\n size=\"small\"\n color={index === 0 ? \"primary\" : \"default\"}\n style={{ marginLeft: 8 }}\n />\n </Box>\n } \n />\n </ListItem>\n {index < data.top_predictions.length - 1 && <Divider />}\n </React.Fragment>\n ))}\n </List>\n ) : (\n <Typography variant=\"body1\">No classifications available</Typography>\n )}\n \n {renderPerformanceInfo()}\n \n {/* Vector DB Actions for ViT Classification */}\n <VectorDBActions results={results} />\n </Paper>\n );\n }\n \n return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n Paper, \n Typography, \n Box, \n TextField, \n Button, \n CircularProgress,\n Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n paper: {\n padding: theme.spacing(2),\n marginTop: theme.spacing(2)\n },\n marginBottom: {\n marginBottom: theme.spacing(2)\n },\n dividerMargin: {\n margin: `${theme.spacing(2)}px 0`\n },\n responseBox: {\n padding: theme.spacing(2),\n backgroundColor: '#f5f5f5',\n borderRadius: theme.shape.borderRadius,\n marginTop: theme.spacing(2),\n whiteSpace: 'pre-wrap'\n },\n buttonProgress: {\n marginLeft: theme.spacing(1)\n }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n const classes = useStyles();\n const [userQuery, setUserQuery] = useState('');\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n const [analysisResult, setAnalysisResult] = useState(null);\n const [error, setError] = useState(null);\n\n // Format time for display\n const formatTime = (ms) => {\n if (ms === undefined || ms === null || isNaN(ms)) return '-';\n const num = Number(ms);\n if (num < 1000) return `${num.toFixed(2)} ms`;\n return `${(num / 1000).toFixed(2)} s`;\n };\n\n const handleAnalyze = async () => {\n if (!userQuery.trim()) return;\n \n setIsAnalyzing(true);\n setError(null);\n \n try {\n const response = await fetch('/api/analyze', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n visionResults: visionResults,\n userQuery: userQuery\n }),\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n\n const data = await response.json();\n \n if (data.error) {\n setError(data.error);\n } else {\n setAnalysisResult(data);\n }\n } catch (err) {\n console.error('Error analyzing with LLM:', err);\n setError(`Error analyzing with LLM: ${err.message}`);\n } finally {\n setIsAnalyzing(false);\n }\n };\n\n if (!visionResults) return null;\n\n return (\n <Paper className={classes.paper}>\n <Typography variant=\"h6\" gutterBottom>\n Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n </Typography>\n \n <Typography variant=\"body2\" className={classes.marginBottom}>\n Ask a question about the detected objects or classifications to get an AI-powered analysis.\n </Typography>\n \n <TextField\n fullWidth\n label=\"Your question about the image\"\n variant=\"outlined\"\n value={userQuery}\n onChange={(e) => setUserQuery(e.target.value)}\n disabled={isAnalyzing}\n className={classes.marginBottom}\n placeholder={model === 'vit' \n ? \"E.g., What category does this image belong to?\" \n : \"E.g., How many people are in this image?\"}\n />\n \n <Button \n variant=\"contained\" \n color=\"primary\"\n onClick={handleAnalyze}\n disabled={isAnalyzing || !userQuery.trim()}\n >\n Analyze with AI\n {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n </Button>\n \n {error && (\n <Box mt={2}>\n <Typography color=\"error\">{error}</Typography>\n </Box>\n )}\n \n {analysisResult && (\n <>\n <Divider className={classes.dividerMargin} />\n \n <Typography variant=\"subtitle1\" gutterBottom>\n AI Analysis:\n </Typography>\n \n <Box className={classes.responseBox}>\n <Typography variant=\"body1\">\n {analysisResult.response}\n </Typography>\n </Box>\n \n {analysisResult.performance && (\n <Box mt={1}>\n <Typography variant=\"body2\" color=\"textSecondary\">\n Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n </Typography>\n </Box>\n )}\n </>\n )}\n </Paper>\n );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport { \n Paper,\n Typography,\n Grid,\n TextField,\n Button,\n Divider\n} from '@material-ui/core';\n\nfunction OpenAIChat() {\n const [model, setModel] = useState('gpt-4');\n const [apiKey, setApiKey] = useState('');\n const [system, setSystem] = useState('You are a helpful assistant.');\n const [prompt, setPrompt] = useState('');\n const [response, setResponse] = useState('');\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState('');\n\n const onSend = async () => {\n setError('');\n setResponse('');\n const p = (prompt || '').trim();\n if (!p) { setError('Please enter a question.'); return; }\n\n setLoading(true);\n try {\n const res = await fetch('/api/openai/chat', {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n credentials: 'include',\n body: JSON.stringify({\n prompt: p,\n model: (model || '').trim() || 'gpt-4',\n api_key: (apiKey || undefined),\n system: (system || undefined)\n })\n });\n\n if (!res.ok) {\n let txt = await res.text();\n try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n throw new Error(txt);\n }\n const data = await res.json();\n const meta = `Model: ${data.model} | Latency: ${data.latency_sec}s` + (data.usage ? ` | Usage: ${JSON.stringify(data.usage)}` : '');\n setResponse((data.response || '(Empty response)') + '\\n\\n---\\n' + meta);\n } catch (e) {\n setError('Error: ' + e.message);\n } finally {\n setLoading(false);\n }\n };\n\n const onClear = () => {\n setPrompt('');\n setResponse('');\n setError('');\n };\n\n return (\n <Paper style={{ padding: 16 }}>\n <Typography variant=\"h5\" gutterBottom>\n OpenAI Chat (OpenAI API)\n </Typography>\n <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n If the server env var OPENAI_API_KEY is set, the API Key field is optional.\n </Typography>\n <Grid container spacing={2}>\n <Grid item xs={12} md={6}>\n <TextField\n label=\"Model\"\n value={model}\n onChange={(e) => setModel(e.target.value)}\n fullWidth\n variant=\"outlined\"\n size=\"small\"\n />\n </Grid>\n <Grid item xs={12} md={6}>\n <TextField\n label=\"OpenAI API Key (optional)\"\n value={apiKey}\n onChange={(e) => setApiKey(e.target.value)}\n fullWidth\n variant=\"outlined\"\n size=\"small\"\n type=\"password\"\n placeholder=\"sk-...\"\n />\n </Grid>\n <Grid item xs={12}>\n <TextField\n label=\"System Prompt (optional)\"\n value={system}\n onChange={(e) => setSystem(e.target.value)}\n fullWidth\n variant=\"outlined\"\n size=\"small\"\n />\n </Grid>\n <Grid item xs={12}>\n <TextField\n label=\"User Question\"\n value={prompt}\n onChange={(e) => setPrompt(e.target.value)}\n fullWidth\n multiline\n rows={4}\n variant=\"outlined\"\n />\n </Grid>\n {error && (\n <Grid item xs={12}>\n <Typography color=\"error\">{error}</Typography>\n </Grid>\n )}\n <Grid item xs={12}>\n <div style={{ display: 'flex', gap: 8 }}>\n <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n {loading ? 'Sending...' : 'Send Question'}\n </Button>\n <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n </div>\n </Grid>\n <Grid item xs={12}>\n <Divider style={{ margin: '12px 0' }} />\n <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n </Grid>\n </Grid>\n </Paper>\n );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n Container, \n Typography, \n Box, \n Paper, \n Grid, \n CircularProgress,\n AppBar,\n Toolbar,\n ThemeProvider,\n createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n palette: {\n primary: {\n main: '#3f51b5',\n },\n secondary: {\n main: '#f50057',\n },\n },\n typography: {\n fontFamily: 'Roboto, Arial, sans-serif',\n },\n});\n\nfunction App() {\n const [selectedImage, setSelectedImage] = useState(null);\n const [selectedModel, setSelectedModel] = useState('');\n const [isProcessing, setIsProcessing] = useState(false);\n const [results, setResults] = useState(null);\n const [error, setError] = useState(null);\n const [modelsStatus, setModelsStatus] = useState({\n yolo: false,\n detr: false,\n vit: false\n });\n\n // Check API status on component mount\n useEffect(() => {\n fetch('/api/status')\n .then(response => response.json())\n .then(data => {\n setModelsStatus(data.models);\n })\n .catch(err => {\n console.error('Error checking API status:', err);\n setError('Error connecting to the backend API. Please make sure the server is running.');\n });\n }, []);\n\n const handleImageUpload = (image) => {\n setSelectedImage(image);\n setResults(null);\n setError(null);\n };\n\n const handleModelSelect = (model) => {\n setSelectedModel(model);\n setResults(null);\n setError(null);\n };\n\n const processImage = async () => {\n if (!selectedImage || !selectedModel) {\n setError('Please select both an image and a model');\n return;\n }\n\n setIsProcessing(true);\n setError(null);\n\n // Create form data for the image\n const formData = new FormData();\n formData.append('image', selectedImage);\n\n let endpoint = '';\n switch (selectedModel) {\n case 'yolo':\n endpoint = '/api/detect/yolo';\n break;\n case 'detr':\n endpoint = '/api/detect/detr';\n break;\n case 'vit':\n endpoint = '/api/classify/vit';\n break;\n default:\n setError('Invalid model selection');\n setIsProcessing(false);\n return;\n }\n\n try {\n const response = await fetch(endpoint, {\n method: 'POST',\n body: formData,\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n\n const data = await response.json();\n setResults({ model: selectedModel, data });\n } catch (err) {\n console.error('Error processing image:', err);\n setError(`Error processing image: ${err.message}`);\n } finally {\n setIsProcessing(false);\n }\n };\n\n return (\n <ThemeProvider theme={theme}>\n <Box style={{ flexGrow: 1 }}>\n <AppBar position=\"static\">\n <Toolbar>\n <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n Multi-Model Object Detection Demo\n </Typography>\n </Toolbar>\n </AppBar>\n <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n <Grid container spacing={3}>\n <Grid item xs={12}>\n <Paper style={{ padding: theme.spacing(2) }}>\n <Typography variant=\"h5\" gutterBottom>\n Upload an image to see how each model performs!\n </Typography>\n <Typography variant=\"body1\" paragraph>\n This demo showcases three different object detection and image classification models:\n </Typography>\n <Typography variant=\"body1\" component=\"div\">\n <ul>\n <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n </ul>\n </Typography>\n </Paper>\n </Grid>\n \n <Grid item xs={12} md={6}>\n <ImageUploader onImageUpload={handleImageUpload} />\n </Grid>\n \n <Grid item xs={12} md={6}>\n <ModelSelector \n onModelSelect={handleModelSelect} \n onProcess={processImage}\n isProcessing={isProcessing}\n modelsStatus={modelsStatus}\n selectedModel={selectedModel}\n imageSelected={!!selectedImage}\n />\n </Grid>\n \n {error && (\n <Grid item xs={12}>\n <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n <Typography color=\"error\">{error}</Typography>\n </Paper>\n </Grid>\n )}\n \n {isProcessing && (\n <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n <CircularProgress />\n <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n Processing image...\n </Typography>\n </Grid>\n )}\n \n {results && (\n <>\n <Grid item xs={12}>\n <ResultDisplay results={results} />\n </Grid>\n <Grid item xs={12}>\n <LlmAnalysis visionResults={results.data} model={results.model} />\n </Grid>\n </>\n )}\n\n {/* OpenAI Chat section at the end */}\n <Grid item xs={12}>\n <OpenAIChat />\n </Grid>\n </Grid>\n </Container>\n </Box>\n </ThemeProvider>\n );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n if (onPerfEntry && onPerfEntry instanceof Function) {\n import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n getCLS(onPerfEntry);\n getFID(onPerfEntry);\n getFCP(onPerfEntry);\n getLCP(onPerfEntry);\n getTTFB(onPerfEntry);\n });\n }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n <React.StrictMode>\n <App />\n </React.StrictMode>,\n document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}

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React, { useState, useRef } from 'react';\nimport { \n Paper, \n Typography, \n Box, \n Button, \n IconButton \n} from '@material-ui/core';\nimport CloudUploadIcon from '@material-ui/icons/CloudUpload';\nimport DeleteIcon from '@material-ui/icons/Delete';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n paper: {\n padding: theme.spacing(2),\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n height: '100%',\n minHeight: 300,\n transition: 'all 0.3s ease'\n },\n dragActive: {\n border: '2px dashed #3f51b5',\n backgroundColor: 'rgba(63, 81, 181, 0.05)'\n },\n dragInactive: {\n border: '2px dashed #ccc',\n backgroundColor: 'white'\n },\n uploadBox: {\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n justifyContent: 'center',\n height: '100%',\n width: '100%',\n cursor: 'pointer'\n },\n uploadIcon: {\n fontSize: 60,\n color: '#3f51b5',\n marginBottom: theme.spacing(2)\n },\n supportText: {\n marginTop: theme.spacing(2)\n },\n previewBox: {\n display: 'flex',\n flexDirection: 'column',\n alignItems: 'center',\n width: '100%',\n height: '100%',\n position: 'relative'\n },\n imageContainer: {\n position: 'relative',\n width: '100%',\n // Use viewport-based height so any aspect ratio fits inside\n height: '60vh',\n [theme.breakpoints.down('sm')]: {\n height: '45vh',\n },\n display: 'flex',\n justifyContent: 'center',\n alignItems: 'center',\n overflow: 'hidden',\n marginTop: theme.spacing(2),\n },\n deleteButton: {\n position: 'absolute',\n top: 0,\n right: 0,\n backgroundColor: 'rgba(255, 255, 255, 0.7)',\n '&:hover': {\n backgroundColor: 'rgba(255, 255, 255, 0.9)',\n }\n }\n}));\n\nconst ImageUploader = ({ onImageUpload }) => {\n const classes = useStyles();\n const [previewUrl, setPreviewUrl] = useState(null);\n const [dragActive, setDragActive] = useState(false);\n const fileInputRef = useRef(null);\n\n const handleDrag = (e) => {\n e.preventDefault();\n e.stopPropagation();\n if (e.type === 'dragenter' || e.type === 'dragover') {\n setDragActive(true);\n } else if (e.type === 'dragleave') {\n setDragActive(false);\n }\n };\n\n const handleDrop = (e) => {\n e.preventDefault();\n e.stopPropagation();\n setDragActive(false);\n if (e.dataTransfer.files && e.dataTransfer.files[0]) {\n handleFiles(e.dataTransfer.files[0]);\n }\n };\n\n const handleChange = (e) => {\n e.preventDefault();\n if (e.target.files && e.target.files[0]) {\n handleFiles(e.target.files[0]);\n }\n };\n\n const handleFiles = (file) => {\n if (file.type.startsWith('image/')) {\n setPreviewUrl(URL.createObjectURL(file));\n onImageUpload(file);\n } else {\n alert('Please upload an image file');\n }\n };\n\n const onButtonClick = () => {\n fileInputRef.current.click();\n };\n\n const handleRemoveImage = () => {\n setPreviewUrl(null);\n onImageUpload(null);\n fileInputRef.current.value = \"\";\n };\n\n return (\n <Paper \n className={`${classes.paper} ${dragActive ? classes.dragActive : classes.dragInactive}`}\n onDragEnter={handleDrag}\n onDragLeave={handleDrag}\n onDragOver={handleDrag}\n onDrop={handleDrop}\n >\n <input\n ref={fileInputRef}\n type=\"file\"\n accept=\"image/*\"\n onChange={handleChange}\n style={{ display: 'none' }}\n />\n\n {!previewUrl ? (\n <Box \n className={classes.uploadBox}\n onClick={onButtonClick}\n >\n <CloudUploadIcon className={classes.uploadIcon} />\n <Typography variant=\"h6\" gutterBottom>\n Drag & Drop an image here\n </Typography>\n <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n or\n </Typography>\n <Button\n variant=\"contained\"\n color=\"primary\"\n component=\"span\"\n startIcon={<CloudUploadIcon />}\n >\n Browse Files\n </Button>\n <Typography variant=\"body2\" color=\"textSecondary\" className={classes.supportText}>\n Supported formats: JPG, PNG, GIF\n </Typography>\n </Box>\n ) : (\n <Box className={classes.previewBox}>\n <Typography variant=\"h6\" gutterBottom>\n Preview\n </Typography>\n <Box className={classes.imageContainer}>\n <img\n src={previewUrl}\n alt=\"Preview\"\n className=\"preview-image\"\n style={{\n width: '100%',\n height: 'auto',\n maxHeight: '100%',\n objectFit: 'contain',\n display: 'block'\n }}\n />\n <IconButton\n aria-label=\"delete\"\n className={classes.deleteButton}\n onClick={handleRemoveImage}\n >\n <DeleteIcon />\n </IconButton>\n </Box>\n </Box>\n )}\n </Paper>\n );\n};\n\nexport default ImageUploader;\n","import React from 'react';\nimport { \n Grid, \n Card, \n CardContent, \n CardActions, \n Typography, \n Button, \n Chip,\n Box\n} from '@material-ui/core';\nimport VisibilityIcon from '@material-ui/icons/Visibility';\nimport CategoryIcon from '@material-ui/icons/Category';\nimport PlayArrowIcon from '@material-ui/icons/PlayArrow';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n card: {\n height: '100%',\n display: 'flex',\n flexDirection: 'column',\n },\n selectedCard: {\n border: '2px solid #3f51b5',\n },\n unavailableCard: {\n opacity: 0.6,\n },\n cardContent: {\n flexGrow: 1,\n },\n chipContainer: {\n marginBottom: theme.spacing(1.5),\n },\n successChip: {\n backgroundColor: '#34C759',\n color: '#fff',\n },\n errorChip: {\n backgroundColor: '#FF3B3F',\n color: '#fff',\n },\n modelType: {\n marginTop: theme.spacing(1),\n },\n processButton: {\n marginTop: theme.spacing(3),\n textAlign: 'center',\n }\n}));\n\nconst ModelSelector = ({ \n onModelSelect, \n onProcess, \n isProcessing, \n modelsStatus, \n selectedModel,\n imageSelected \n}) => {\n const classes = useStyles();\n \n const models = [\n {\n id: 'yolo',\n name: 'YOLOv8',\n description: 'Fast and accurate object detection',\n icon: <VisibilityIcon />,\n available: modelsStatus.yolo\n },\n {\n id: 'detr',\n name: 'DETR',\n description: 'DEtection TRansformer for object detection',\n icon: <VisibilityIcon />,\n available: modelsStatus.detr\n },\n {\n id: 'vit',\n name: 'ViT',\n description: 'Vision Transformer for image classification',\n icon: <CategoryIcon />,\n available: modelsStatus.vit\n }\n ];\n\n const handleModelClick = (modelId) => {\n if (models.find(m => m.id === modelId).available) {\n onModelSelect(modelId);\n }\n };\n\n return (\n <Box sx={{ p: 2, height: '100%' }}>\n <Typography variant=\"h6\" gutterBottom>\n Select Model\n </Typography>\n \n <Grid container spacing={2}>\n {models.map((model) => (\n <Grid item xs={12} sm={4} key={model.id}>\n <Card \n className={`\n ${classes.card} \n ${selectedModel === model.id ? classes.selectedCard : ''} \n ${!model.available ? classes.unavailableCard : ''}\n `}\n onClick={() => handleModelClick(model.id)}\n >\n <CardContent className={classes.cardContent}>\n <Box sx={{ mb: 2, color: 'primary' }}>\n {model.icon}\n </Box>\n <Typography variant=\"h5\" component=\"div\" gutterBottom>\n {model.name}\n </Typography>\n <div className={classes.chipContainer}>\n {model.available ? (\n <Chip \n label=\"Available\" \n className={classes.successChip}\n size=\"small\" \n />\n ) : (\n <Chip \n label=\"Not Available\" \n className={classes.errorChip}\n size=\"small\" \n />\n )}\n </div>\n <Typography variant=\"body2\" color=\"textSecondary\">\n {model.description}\n </Typography>\n </CardContent>\n <CardActions>\n <Button \n size=\"small\" \n onClick={() => handleModelClick(model.id)}\n disabled={!model.available}\n color={selectedModel === model.id ? \"primary\" : \"default\"}\n variant={selectedModel === model.id ? \"contained\" : \"outlined\"}\n fullWidth\n >\n {selectedModel === model.id ? 'Selected' : 'Select'}\n </Button>\n </CardActions>\n </Card>\n </Grid>\n ))}\n </Grid>\n\n <div className={classes.processButton}>\n <Button\n variant=\"contained\"\n color=\"primary\"\n size=\"large\"\n startIcon={<PlayArrowIcon />}\n onClick={onProcess}\n disabled={!selectedModel || !imageSelected || isProcessing}\n >\n {isProcessing ? 'Processing...' : 'Process Image'}\n </Button>\n </div>\n </Box>\n );\n};\n\nexport default ModelSelector;\n","import React, { useState } from 'react';\nimport { \n Button, \n Box, \n Typography, \n CircularProgress, \n Snackbar,\n Dialog,\n DialogTitle,\n DialogContent,\n DialogActions,\n TextField,\n FormControl,\n InputLabel,\n Select,\n MenuItem,\n Grid,\n Card,\n CardMedia,\n CardContent,\n Chip\n} from '@material-ui/core';\nimport { Alert } from '@material-ui/lab';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n root: {\n marginTop: theme.spacing(2),\n marginBottom: theme.spacing(2),\n padding: theme.spacing(2),\n backgroundColor: '#f5f5f5',\n borderRadius: theme.shape.borderRadius,\n },\n button: {\n marginRight: theme.spacing(2),\n },\n searchDialog: {\n minWidth: '500px',\n },\n formControl: {\n marginBottom: theme.spacing(2),\n minWidth: '100%',\n },\n searchResults: {\n marginTop: theme.spacing(2),\n },\n resultCard: {\n marginBottom: theme.spacing(2),\n },\n resultImage: {\n height: 140,\n objectFit: 'contain',\n },\n chip: {\n margin: theme.spacing(0.5),\n },\n similarityChip: {\n backgroundColor: theme.palette.primary.main,\n color: 'white',\n }\n}));\n\nconst VectorDBActions = ({ results }) => {\n const classes = useStyles();\n const [isSaving, setIsSaving] = useState(false);\n const [saveSuccess, setSaveSuccess] = useState(false);\n const [saveError, setSaveError] = useState(null);\n const [openSearchDialog, setOpenSearchDialog] = useState(false);\n const [searchType, setSearchType] = useState('image');\n const [searchClass, setSearchClass] = useState('');\n const [searchResults, setSearchResults] = useState([]);\n const [isSearching, setIsSearching] = useState(false);\n const [searchError, setSearchError] = useState(null);\n \n // Extract model and data from results\n const { model, data } = results;\n \n // Handle saving to vector DB\n const handleSaveToVectorDB = async () => {\n setIsSaving(true);\n setSaveError(null);\n \n try {\n let response;\n \n if (model === 'vit') {\n // For ViT, save the whole image with classifications\n response = await fetch('/api/add-to-collection', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n image: data.image,\n metadata: {\n model: 'vit',\n classifications: data.classifications\n }\n })\n });\n } else {\n // For YOLO and DETR, save detected objects\n response = await fetch('/api/add-detected-objects', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n image: data.image,\n objects: data.detections,\n imageId: generateUUID()\n })\n });\n }\n \n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n \n const result = await response.json();\n \n if (result.error) {\n throw new Error(result.error);\n }\n \n setSaveSuccess(true);\n setTimeout(() => setSaveSuccess(false), 5000);\n } catch (err) {\n console.error('Error saving to vector DB:', err);\n setSaveError(`Error saving to vector DB: ${err.message}`);\n } finally {\n setIsSaving(false);\n }\n };\n \n // Handle opening search dialog\n const handleOpenSearchDialog = () => {\n setOpenSearchDialog(true);\n setSearchResults([]);\n setSearchError(null);\n };\n \n // Handle closing search dialog\n const handleCloseSearchDialog = () => {\n setOpenSearchDialog(false);\n };\n \n // Handle search type change\n const handleSearchTypeChange = (event) => {\n setSearchType(event.target.value);\n setSearchResults([]);\n setSearchError(null);\n };\n \n // Handle search class change\n const handleSearchClassChange = (event) => {\n setSearchClass(event.target.value);\n };\n \n // Handle search\n const handleSearch = async () => {\n setIsSearching(true);\n setSearchError(null);\n \n try {\n let requestBody = {};\n \n if (searchType === 'image') {\n // Search by current image\n requestBody = {\n searchType: 'image',\n image: data.image,\n n_results: 5\n };\n } else {\n // Search by class name\n if (!searchClass.trim()) {\n throw new Error('Please enter a class name');\n }\n \n requestBody = {\n searchType: 'class',\n class_name: searchClass.trim(),\n n_results: 5\n };\n }\n \n const response = await fetch('/api/search-similar-objects', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(requestBody)\n });\n \n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n \n const result = await response.json();\n \n if (result.error) {\n throw new Error(result.error);\n }\n \n console.log('Search API response:', result);\n \n // The backend responds with {success, searchType, results} structure, so extract only the results array\n if (result.success && Array.isArray(result.results)) {\n console.log('Setting search results array:', result.results);\n console.log('Results array length:', result.results.length);\n console.log('First result item:', result.results[0]);\n setSearchResults(result.results);\n } else {\n console.error('Unexpected API response format:', result);\n throw new Error('Unexpected API response format');\n }\n } catch (err) {\n console.error('Error searching vector DB:', err);\n setSearchError(`Error searching vector DB: ${err.message}`);\n } finally {\n setIsSearching(false);\n }\n };\n \n // Generate UUID for image ID\n const generateUUID = () => {\n return 'xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx'.replace(/[xy]/g, function(c) {\n const r = Math.random() * 16 | 0;\n const v = c === 'x' ? r : (r & 0x3 | 0x8);\n return v.toString(16);\n });\n };\n \n // Render search results\n const renderSearchResults = () => {\n console.log('Rendering search results:', searchResults);\n console.log('Search results length:', searchResults.length);\n \n if (searchResults.length === 0) {\n console.log('No results to render');\n return (\n <Typography variant=\"body1\">No results found.</Typography>\n );\n }\n \n return (\n <Grid container spacing={2}>\n {searchResults.map((result, index) => {\n const similarity = (1 - result.distance) * 100;\n \n return (\n <Grid item xs={12} sm={6} key={index}>\n <Card className={classes.resultCard}>\n {result.metadata && result.metadata.image_data ? (\n <CardMedia\n className={classes.resultImage}\n component=\"img\"\n height=\"200\"\n image={`data:image/jpeg;base64,${result.metadata.image_data}`}\n alt={result.metadata && result.metadata.class ? result.metadata.class : 'Object'}\n />\n ) : (\n <Box \n className={classes.resultImage}\n style={{ \n backgroundColor: '#f0f0f0', \n display: 'flex', \n alignItems: 'center', \n justifyContent: 'center',\n height: 200\n }}\n >\n <Typography variant=\"body2\" color=\"textSecondary\">\n {result.metadata && result.metadata.class ? result.metadata.class : 'Object'} Image\n </Typography>\n </Box>\n )}\n <CardContent>\n <Box display=\"flex\" justifyContent=\"space-between\" alignItems=\"center\" mb={1}>\n <Typography variant=\"subtitle1\">Result #{index + 1}</Typography>\n <Chip \n label={`Similarity: ${similarity.toFixed(2)}%`}\n className={classes.similarityChip}\n size=\"small\"\n />\n </Box>\n <Typography variant=\"body2\" color=\"textSecondary\">\n <strong>Class:</strong> {result.metadata.class || 'N/A'}\n </Typography>\n {result.metadata.confidence && (\n <Typography variant=\"body2\" color=\"textSecondary\">\n <strong>Confidence:</strong> {(result.metadata.confidence * 100).toFixed(2)}%\n </Typography>\n )}\n <Typography variant=\"body2\" color=\"textSecondary\">\n <strong>Object ID:</strong> {result.id}\n </Typography>\n </CardContent>\n </Card>\n </Grid>\n );\n })}\n </Grid>\n );\n };\n \n return (\n <Box className={classes.root}>\n <Typography variant=\"h6\" gutterBottom>\n Vector Database Actions\n </Typography>\n \n <Box display=\"flex\" alignItems=\"center\" mb={2}>\n <Button\n variant=\"contained\"\n color=\"primary\"\n onClick={handleSaveToVectorDB}\n disabled={isSaving}\n className={classes.button}\n >\n {isSaving ? (\n <>\n <CircularProgress size={20} color=\"inherit\" style={{ marginRight: 8 }} />\n Saving...\n </>\n ) : (\n 'Save to Vector DB'\n )}\n </Button>\n \n <Button\n variant=\"outlined\"\n color=\"primary\"\n onClick={handleOpenSearchDialog}\n className={classes.button}\n >\n Search Similar\n </Button>\n </Box>\n \n {saveError && (\n <Alert severity=\"error\" style={{ marginTop: 8 }}>\n {saveError}\n </Alert>\n )}\n \n <Snackbar open={saveSuccess} autoHideDuration={5000} onClose={() => setSaveSuccess(false)}>\n <Alert severity=\"success\">\n {model === 'vit' ? (\n 'Image and classifications successfully saved to vector DB!'\n ) : (\n 'Detected objects successfully saved to vector DB!'\n )}\n </Alert>\n </Snackbar>\n \n {/* Search Dialog */}\n <Dialog\n open={openSearchDialog}\n onClose={handleCloseSearchDialog}\n maxWidth=\"md\"\n fullWidth\n >\n <DialogTitle>Search Vector Database</DialogTitle>\n <DialogContent>\n <FormControl className={classes.formControl}>\n <InputLabel id=\"search-type-label\">Search Type</InputLabel>\n <Select\n labelId=\"search-type-label\"\n id=\"search-type\"\n value={searchType}\n onChange={handleSearchTypeChange}\n >\n <MenuItem value=\"image\">Search by Current Image</MenuItem>\n <MenuItem value=\"class\">Search by Class Name</MenuItem>\n </Select>\n </FormControl>\n \n {searchType === 'class' && (\n <FormControl className={classes.formControl}>\n <TextField\n label=\"Class Name\"\n value={searchClass}\n onChange={handleSearchClassChange}\n placeholder=\"e.g. person, car, dog...\"\n fullWidth\n />\n </FormControl>\n )}\n \n {searchError && (\n <Alert severity=\"error\" style={{ marginBottom: 16 }}>\n {searchError}\n </Alert>\n )}\n \n <Box className={classes.searchResults}>\n {isSearching ? (\n <Box display=\"flex\" justifyContent=\"center\" alignItems=\"center\" p={4}>\n <CircularProgress />\n <Typography variant=\"body1\" style={{ marginLeft: 16 }}>\n Searching...\n </Typography>\n </Box>\n ) : (\n <>\n {console.log('Search dialog render - searchResults:', searchResults)}\n {searchResults.length > 0 ? renderSearchResults() : \n <Typography variant=\"body1\">No results found. Please try another search.</Typography>\n }\n </>\n )}\n </Box>\n </DialogContent>\n <DialogActions>\n <Button onClick={handleCloseSearchDialog} color=\"default\">\n Close\n </Button>\n <Button \n onClick={handleSearch} \n color=\"primary\" \n variant=\"contained\"\n disabled={isSearching || (searchType === 'class' && !searchClass.trim())}\n >\n Search\n </Button>\n </DialogActions>\n </Dialog>\n </Box>\n );\n};\n\nexport default VectorDBActions;\n","import React from 'react';\nimport { \n Paper, \n Typography, \n Box, \n List, \n ListItem, \n ListItemText, \n Divider,\n Grid,\n Chip\n} from '@material-ui/core';\nimport VectorDBActions from './VectorDBActions';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n paper: {\n padding: theme.spacing(2)\n },\n marginBottom: {\n marginBottom: theme.spacing(2)\n },\n resultImage: {\n maxWidth: '100%',\n maxHeight: '400px',\n objectFit: 'contain'\n },\n dividerMargin: {\n margin: `${theme.spacing(2)}px 0`\n },\n chipContainer: {\n display: 'flex',\n gap: theme.spacing(1),\n flexWrap: 'wrap'\n }\n}));\n\nconst ResultDisplay = ({ results }) => {\n const classes = useStyles();\n if (!results) return null;\n \n const { model, data } = results;\n \n // Helper to format times nicely\n const formatTime = (ms) => {\n if (ms === undefined || ms === null || isNaN(ms)) return '-';\n const num = Number(ms);\n if (num < 1000) return `${num.toFixed(2)} ms`;\n return `${(num / 1000).toFixed(2)} s`;\n };\n \n // Check if there's an error\n if (data.error) {\n return (\n <Paper sx={{ p: 2, bgcolor: '#ffebee' }}>\n <Typography color=\"error\">{data.error}</Typography>\n </Paper>\n );\n }\n\n // Display performance info\n const renderPerformanceInfo = () => {\n if (!data.performance) return null;\n \n return (\n <Box className=\"performance-info\">\n <Divider className={classes.dividerMargin} />\n <Typography variant=\"body2\">\n Inference time: {formatTime(data.performance.inference_time)} on {data.performance.device}\n </Typography>\n </Box>\n );\n };\n\n // Render for YOLO and DETR (object detection)\n if (model === 'yolo' || model === 'detr') {\n return (\n <Paper className={classes.paper}>\n <Typography variant=\"h6\" gutterBottom>\n {model === 'yolo' ? 'YOLOv8' : 'DETR'} Detection Results\n </Typography>\n \n <Grid container spacing={3}>\n <Grid item xs={12} md={6}>\n {data.image && (\n <Box className={classes.marginBottom}>\n <Typography variant=\"subtitle1\" gutterBottom>\n Detection Result\n </Typography>\n <img \n src={`data:image/png;base64,${data.image}`} \n alt=\"Detection Result\" \n className={classes.resultImage}\n />\n </Box>\n )}\n </Grid>\n \n <Grid item xs={12} md={6}>\n <Box className={classes.marginBottom}>\n <Typography variant=\"subtitle1\" gutterBottom>\n Detected Objects:\n </Typography>\n \n {data.detections && data.detections.length > 0 ? (\n <List>\n {data.detections.map((detection, index) => (\n <React.Fragment key={index}>\n <ListItem>\n <ListItemText \n primary={\n <Box style={{ display: 'flex', alignItems: 'center' }}>\n <Typography variant=\"body1\" component=\"span\">\n {detection.class}\n </Typography>\n <Chip \n label={`${(detection.confidence * 100).toFixed(0)}%`}\n size=\"small\"\n color=\"primary\"\n style={{ marginLeft: 8 }}\n />\n </Box>\n } \n secondary={`Bounding Box: [${detection.bbox.join(', ')}]`} \n />\n </ListItem>\n {index < data.detections.length - 1 && <Divider />}\n </React.Fragment>\n ))}\n </List>\n ) : (\n <Typography variant=\"body1\">No objects detected</Typography>\n )}\n </Box>\n </Grid>\n </Grid>\n \n {renderPerformanceInfo()}\n \n {/* Vector DB Actions for Object Detection */}\n <VectorDBActions results={results} />\n </Paper>\n );\n }\n \n // Render for ViT (classification)\n if (model === 'vit') {\n return (\n <Paper className={classes.paper}>\n <Typography variant=\"h6\" gutterBottom>\n ViT Classification Results\n </Typography>\n \n <Typography variant=\"subtitle1\" gutterBottom>\n Top Predictions:\n </Typography>\n \n {data.top_predictions && data.top_predictions.length > 0 ? (\n <List>\n {data.top_predictions.map((prediction, index) => (\n <React.Fragment key={index}>\n <ListItem>\n <ListItemText \n primary={\n <Box style={{ display: 'flex', alignItems: 'center' }}>\n <Typography variant=\"body1\" component=\"span\">\n {prediction.rank}. {prediction.class}\n </Typography>\n <Chip \n label={`${(prediction.probability * 100).toFixed(1)}%`}\n size=\"small\"\n color={index === 0 ? \"primary\" : \"default\"}\n style={{ marginLeft: 8 }}\n />\n </Box>\n } \n />\n </ListItem>\n {index < data.top_predictions.length - 1 && <Divider />}\n </React.Fragment>\n ))}\n </List>\n ) : (\n <Typography variant=\"body1\">No classifications available</Typography>\n )}\n \n {renderPerformanceInfo()}\n \n {/* Vector DB Actions for ViT Classification */}\n <VectorDBActions results={results} />\n </Paper>\n );\n }\n \n return null;\n};\n\nexport default ResultDisplay;\n","import React, { useState } from 'react';\nimport { \n Paper, \n Typography, \n Box, \n TextField, \n Button, \n CircularProgress,\n Divider\n} from '@material-ui/core';\nimport { makeStyles } from '@material-ui/core/styles';\n\nconst useStyles = makeStyles((theme) => ({\n paper: {\n padding: theme.spacing(2),\n marginTop: theme.spacing(2)\n },\n marginBottom: {\n marginBottom: theme.spacing(2)\n },\n dividerMargin: {\n margin: `${theme.spacing(2)}px 0`\n },\n responseBox: {\n padding: theme.spacing(2),\n backgroundColor: '#f5f5f5',\n borderRadius: theme.shape.borderRadius,\n marginTop: theme.spacing(2),\n whiteSpace: 'pre-wrap'\n },\n buttonProgress: {\n marginLeft: theme.spacing(1)\n }\n}));\n\nconst LlmAnalysis = ({ visionResults, model }) => {\n const classes = useStyles();\n const [userQuery, setUserQuery] = useState('');\n const [isAnalyzing, setIsAnalyzing] = useState(false);\n const [analysisResult, setAnalysisResult] = useState(null);\n const [error, setError] = useState(null);\n\n // Format time for display\n const formatTime = (ms) => {\n if (ms === undefined || ms === null || isNaN(ms)) return '-';\n const num = Number(ms);\n if (num < 1000) return `${num.toFixed(2)} ms`;\n return `${(num / 1000).toFixed(2)} s`;\n };\n\n const handleAnalyze = async () => {\n if (!userQuery.trim()) return;\n \n setIsAnalyzing(true);\n setError(null);\n \n try {\n const response = await fetch('/api/analyze', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n visionResults: visionResults,\n userQuery: userQuery\n }),\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n\n const data = await response.json();\n \n if (data.error) {\n setError(data.error);\n } else {\n setAnalysisResult(data);\n }\n } catch (err) {\n console.error('Error analyzing with LLM:', err);\n setError(`Error analyzing with LLM: ${err.message}`);\n } finally {\n setIsAnalyzing(false);\n }\n };\n\n if (!visionResults) return null;\n\n return (\n <Paper className={classes.paper}>\n <Typography variant=\"h6\" gutterBottom>\n Ask AI about the {model === 'vit' ? 'Classification' : 'Detection'} Results\n </Typography>\n \n <Typography variant=\"body2\" className={classes.marginBottom}>\n Ask a question about the detected objects or classifications to get an AI-powered analysis.\n </Typography>\n \n <TextField\n fullWidth\n label=\"Your question about the image\"\n variant=\"outlined\"\n value={userQuery}\n onChange={(e) => setUserQuery(e.target.value)}\n disabled={isAnalyzing}\n className={classes.marginBottom}\n placeholder={model === 'vit' \n ? \"E.g., What category does this image belong to?\" \n : \"E.g., How many people are in this image?\"}\n />\n \n <Button \n variant=\"contained\" \n color=\"primary\"\n onClick={handleAnalyze}\n disabled={isAnalyzing || !userQuery.trim()}\n >\n Analyze with AI\n {isAnalyzing && <CircularProgress size={24} className={classes.buttonProgress} />}\n </Button>\n \n {error && (\n <Box mt={2}>\n <Typography color=\"error\">{error}</Typography>\n </Box>\n )}\n \n {analysisResult && (\n <>\n <Divider className={classes.dividerMargin} />\n \n <Typography variant=\"subtitle1\" gutterBottom>\n AI Analysis:\n </Typography>\n \n <Box className={classes.responseBox}>\n <Typography variant=\"body1\">\n {analysisResult.response}\n </Typography>\n </Box>\n \n {analysisResult.performance && (\n <Box mt={1}>\n <Typography variant=\"body2\" color=\"textSecondary\">\n Analysis time: {formatTime(analysisResult.performance.inference_time)} on {analysisResult.performance.device}\n </Typography>\n </Box>\n )}\n </>\n )}\n </Paper>\n );\n};\n\nexport default LlmAnalysis;\n","import React, { useState } from 'react';\nimport { \n Paper,\n Typography,\n Grid,\n TextField,\n Button,\n Divider\n} from '@material-ui/core';\n\nfunction OpenAIChat() {\n const [model, setModel] = useState('gpt-4');\n const [apiKey, setApiKey] = useState('');\n const [system, setSystem] = useState('You are a helpful assistant.');\n const [prompt, setPrompt] = useState('');\n const [response, setResponse] = useState('');\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState('');\n\n const onSend = async () => {\n setError('');\n setResponse('');\n const p = (prompt || '').trim();\n if (!p) { setError('Please enter a question.'); return; }\n\n setLoading(true);\n try {\n const res = await fetch('/api/openai/chat', {\n method: 'POST',\n headers: { 'Content-Type': 'application/json' },\n credentials: 'include',\n body: JSON.stringify({\n prompt: p,\n model: (model || '').trim() || 'gpt-4',\n api_key: (apiKey || undefined),\n system: (system || undefined)\n })\n });\n\n if (!res.ok) {\n let txt = await res.text();\n try { txt = JSON.stringify(JSON.parse(txt), null, 2); } catch {}\n throw new Error(txt);\n }\n const data = await res.json();\n const meta = `Model: ${data.model} | Latency: ${data.latency_sec}s` + (data.usage ? ` | Usage: ${JSON.stringify(data.usage)}` : '');\n setResponse((data.response || '(Empty response)') + '\\n\\n---\\n' + meta);\n } catch (e) {\n setError('Error: ' + e.message);\n } finally {\n setLoading(false);\n }\n };\n\n const onClear = () => {\n setPrompt('');\n setResponse('');\n setError('');\n };\n\n return (\n <Paper style={{ padding: 16 }}>\n <Typography variant=\"h5\" gutterBottom>\n OpenAI Chat (OpenAI API)\n </Typography>\n <Typography variant=\"body2\" color=\"textSecondary\" gutterBottom>\n If the server env var OPENAI_API_KEY is set, the API Key field is optional.\n </Typography>\n <Grid container spacing={2}>\n <Grid item xs={12} md={6}>\n <TextField\n label=\"Model\"\n value={model}\n onChange={(e) => setModel(e.target.value)}\n fullWidth\n variant=\"outlined\"\n size=\"small\"\n />\n </Grid>\n <Grid item xs={12} md={6}>\n <TextField\n label=\"OpenAI API Key (optional)\"\n value={apiKey}\n onChange={(e) => setApiKey(e.target.value)}\n fullWidth\n variant=\"outlined\"\n size=\"small\"\n type=\"password\"\n placeholder=\"sk-...\"\n />\n </Grid>\n <Grid item xs={12}>\n <TextField\n label=\"System Prompt (optional)\"\n value={system}\n onChange={(e) => setSystem(e.target.value)}\n fullWidth\n variant=\"outlined\"\n size=\"small\"\n />\n </Grid>\n <Grid item xs={12}>\n <TextField\n label=\"User Question\"\n value={prompt}\n onChange={(e) => setPrompt(e.target.value)}\n fullWidth\n multiline\n rows={4}\n variant=\"outlined\"\n />\n </Grid>\n {error && (\n <Grid item xs={12}>\n <Typography color=\"error\">{error}</Typography>\n </Grid>\n )}\n <Grid item xs={12}>\n <div style={{ display: 'flex', gap: 8 }}>\n <Button color=\"primary\" variant=\"contained\" onClick={onSend} disabled={loading}>\n {loading ? 'Sending...' : 'Send Question'}\n </Button>\n <Button variant=\"outlined\" onClick={onClear}>Clear</Button>\n </div>\n </Grid>\n <Grid item xs={12}>\n <Divider style={{ margin: '12px 0' }} />\n <Typography variant=\"subtitle2\" color=\"textSecondary\">Response</Typography>\n <pre style={{ whiteSpace: 'pre-wrap', fontFamily: 'ui-monospace, monospace' }}>{response}</pre>\n </Grid>\n </Grid>\n </Paper>\n );\n}\n\nexport default OpenAIChat;\n","import React, { useState, useEffect } from 'react';\nimport { \n Container, \n Typography, \n Box, \n Paper, \n Grid, \n CircularProgress,\n AppBar,\n Toolbar,\n ThemeProvider,\n createMuiTheme\n} from '@material-ui/core';\nimport ImageUploader from './components/ImageUploader';\nimport ModelSelector from './components/ModelSelector';\nimport ResultDisplay from './components/ResultDisplay';\nimport LlmAnalysis from './components/LlmAnalysis';\nimport OpenAIChat from './components/OpenAIChat';\nimport './App.css';\n\n// Create a theme\nconst theme = createMuiTheme({\n palette: {\n primary: {\n main: '#3f51b5',\n },\n secondary: {\n main: '#f50057',\n },\n },\n typography: {\n fontFamily: 'Roboto, Arial, sans-serif',\n },\n});\n\nfunction App() {\n const [selectedImage, setSelectedImage] = useState(null);\n const [selectedModel, setSelectedModel] = useState('');\n const [isProcessing, setIsProcessing] = useState(false);\n const [results, setResults] = useState(null);\n const [error, setError] = useState(null);\n const [modelsStatus, setModelsStatus] = useState({\n yolo: false,\n detr: false,\n vit: false\n });\n\n // Check API status on component mount\n useEffect(() => {\n fetch('/api/status')\n .then(response => response.json())\n .then(data => {\n setModelsStatus(data.models);\n })\n .catch(err => {\n console.error('Error checking API status:', err);\n setError('Error connecting to the backend API. Please make sure the server is running.');\n });\n }, []);\n\n const handleImageUpload = (image) => {\n setSelectedImage(image);\n setResults(null);\n setError(null);\n };\n\n const handleModelSelect = (model) => {\n setSelectedModel(model);\n setResults(null);\n setError(null);\n };\n\n const processImage = async () => {\n if (!selectedImage || !selectedModel) {\n setError('Please select both an image and a model');\n return;\n }\n\n setIsProcessing(true);\n setError(null);\n\n // Create form data for the image\n const formData = new FormData();\n formData.append('image', selectedImage);\n\n let endpoint = '';\n switch (selectedModel) {\n case 'yolo':\n endpoint = '/api/detect/yolo';\n break;\n case 'detr':\n endpoint = '/api/detect/detr';\n break;\n case 'vit':\n endpoint = '/api/classify/vit';\n break;\n default:\n setError('Invalid model selection');\n setIsProcessing(false);\n return;\n }\n\n try {\n const response = await fetch(endpoint, {\n method: 'POST',\n body: formData,\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! Status: ${response.status}`);\n }\n\n const data = await response.json();\n setResults({ model: selectedModel, data });\n } catch (err) {\n console.error('Error processing image:', err);\n setError(`Error processing image: ${err.message}`);\n } finally {\n setIsProcessing(false);\n }\n };\n\n return (\n <ThemeProvider theme={theme}>\n <Box style={{ flexGrow: 1 }}>\n <AppBar position=\"static\">\n <Toolbar>\n <Typography variant=\"h6\" style={{ flexGrow: 1 }}>\n Multi-Model Object Detection Demo\n </Typography>\n </Toolbar>\n </AppBar>\n <Container maxWidth=\"lg\" style={{ marginTop: theme.spacing(4), marginBottom: theme.spacing(4) }}>\n <Grid container spacing={3}>\n <Grid item xs={12}>\n <Paper style={{ padding: theme.spacing(2) }}>\n <Typography variant=\"h5\" gutterBottom>\n Upload an image to see how each model performs!\n </Typography>\n <Typography variant=\"body1\" paragraph>\n This demo showcases three different object detection and image classification models:\n </Typography>\n <Typography variant=\"body1\" component=\"div\">\n <ul>\n <li><strong>YOLOv8</strong>: Fast and accurate object detection</li>\n <li><strong>DETR</strong>: DEtection TRansformer for object detection</li>\n <li><strong>ViT</strong>: Vision Transformer for image classification</li>\n </ul>\n </Typography>\n </Paper>\n </Grid>\n \n <Grid item xs={12} md={6}>\n <ImageUploader onImageUpload={handleImageUpload} />\n </Grid>\n \n <Grid item xs={12} md={6}>\n <ModelSelector \n onModelSelect={handleModelSelect} \n onProcess={processImage}\n isProcessing={isProcessing}\n modelsStatus={modelsStatus}\n selectedModel={selectedModel}\n imageSelected={!!selectedImage}\n />\n </Grid>\n \n {error && (\n <Grid item xs={12}>\n <Paper style={{ padding: theme.spacing(2), backgroundColor: '#ffebee' }}>\n <Typography color=\"error\">{error}</Typography>\n </Paper>\n </Grid>\n )}\n \n {isProcessing && (\n <Grid item xs={12} style={{ textAlign: 'center', margin: `${theme.spacing(4)}px 0` }}>\n <CircularProgress />\n <Typography variant=\"h6\" style={{ marginTop: theme.spacing(2) }}>\n Processing image...\n </Typography>\n </Grid>\n )}\n \n {results && (\n <>\n <Grid item xs={12}>\n <ResultDisplay results={results} />\n </Grid>\n <Grid item xs={12}>\n <LlmAnalysis visionResults={results.data} model={results.model} />\n </Grid>\n </>\n )}\n\n {/* OpenAI Chat section at the end */}\n <Grid item xs={12}>\n <OpenAIChat />\n </Grid>\n </Grid>\n </Container>\n </Box>\n </ThemeProvider>\n );\n}\n\nexport default App;\n","const reportWebVitals = (onPerfEntry) => {\n if (onPerfEntry && onPerfEntry instanceof Function) {\n import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {\n getCLS(onPerfEntry);\n getFID(onPerfEntry);\n getFCP(onPerfEntry);\n getLCP(onPerfEntry);\n getTTFB(onPerfEntry);\n });\n }\n};\n\nexport default reportWebVitals;\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport reportWebVitals from './reportWebVitals';\n\nReactDOM.render(\n <React.StrictMode>\n <App />\n </React.StrictMode>,\n document.getElementById('root')\n);\n\n// If you want to start measuring performance in your app, pass a function\n// to log results (for example: reportWebVitals(console.log))\n// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals\nreportWebVitals();\n"],"sourceRoot":""}