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Author SHA1 Message Date
Taufik Pathurrohman bd5aa81988 Update validate_onnx_parity.py
Build and Deploy / build (map[dockerfile:apps/api/Dockerfile name:api]) (push) Failing after 3m54s
Build and Deploy / build (map[dockerfile:apps/ml-service/Dockerfile name:ml]) (push) Failing after 41s
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Build and Deploy / deploy (push) Skipped
update code & comment
2026-06-18 21:31:03 +07:00
Luhung Pandyaska Suyi 7877892f9b Add multiple dataset sources to README 2026-06-18 21:27:14 +07:00
Taufik Pathurrohman f8f36bcdb8 Update README.md
Penambahan penjelasan lengkap mengenai Sumber dataset
2026-06-18 21:15:40 +07:00
Selly Supriyatin d1c014d9b3 Merge pull request #45 from ATLAS-PJK-GM007/selly/frontend
feat(auth): add placeholders and helper text to improve form UX
2026-06-16 22:55:34 +07:00
seriouselly 1db8eee8ea feat(auth): add placeholders and helper text to improve form UX
- Add descriptive placeholders to name, email, and password input fields.
- Display a helper text in register mode to guide users on password length requirements.
- Adjust password `minLength` validation in the frontend.
2026-06-16 22:46:06 +07:00
Selly Supriyatin 74e17386ee Merge pull request #44 from ATLAS-PJK-GM007/selly/frontend
feat(ui): add green leaf favicon using SVG data URI
2026-06-16 22:00:36 +07:00
seriouselly a9ef795c90 feat(ui): add green leaf favicon using SVG data URI
- Update index.html to include a Lucide leaf icon as the tab favicon.
- Use URL-encoded SVG data URI to apply the ZeaVis Edu green brand color (#22C55E) directly without needing external image files.
2026-06-16 21:59:43 +07:00
Selly Supriyatin 153abf4352 Merge pull request #43 from ATLAS-PJK-GM007/selly/frontend
feat(scan): implement drag and drop functionality for image upload
2026-06-16 19:20:20 +07:00
seriouselly da5c7c1cfa feat(scan): implement drag and drop functionality for image upload
- Add `onDragOver`, `onDragLeave`, and `onDrop` event handlers to capture dragged files.
- Introduce `isDragging` state to provide visual UI feedback when a file is hovered over the drop zone.
- Wire the dropped file data to the existing `handleFile` processing logic.
2026-06-16 19:12:44 +07:00
MythEclipseandClaude 58d4cc0164 chore: update telemetry submodule and fix Makefile comment
Co-Authored-By: Claude <noreply@anthropic.com>
2026-06-16 16:40:23 +07:00
8 changed files with 160 additions and 48 deletions
File diff suppressed because one or more lines are too long
+18 -2
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@@ -10,10 +10,10 @@ import onnxruntime as ort
import tensorflow as tf import tensorflow as tf
from PIL import Image, UnidentifiedImageError from PIL import Image, UnidentifiedImageError
# Definisi label kelas sesuai urutan output model klasifikasi
LABELS = ["Bercak Daun", "Daun Sehat", "Karat Daun", "Hawar Daun"] LABELS = ["Bercak Daun", "Daun Sehat", "Karat Daun", "Hawar Daun"]
# Kelas eksepsi kustom untuk menangani ketidaksesuaian akurasi prediksi
class ParityError(RuntimeError): class ParityError(RuntimeError):
"""Raised when Keras and ONNX predictions do not match.""" """Raised when Keras and ONNX predictions do not match."""
pass pass
@@ -33,16 +33,19 @@ def preprocess_image(image_path, input_size):
Raises: Raises:
ParityError: If image cannot be loaded or processed. ParityError: If image cannot be loaded or processed.
""" """
# Penanganan error secara aman saat memuat gambar ke format RGB
try: try:
img = Image.open(image_path).convert("RGB") img = Image.open(image_path).convert("RGB")
except (FileNotFoundError, UnidentifiedImageError, OSError) as e: except (FileNotFoundError, UnidentifiedImageError, OSError) as e:
raise ParityError(f"Failed to load image {image_path}: {e}") raise ParityError(f"Failed to load image {image_path}: {e}")
# Penyesuaian resolusi gambar menggunakan metode interpolasi Bilinear
try: try:
img = img.resize((input_size, input_size), Image.Resampling.BILINEAR) img = img.resize((input_size, input_size), Image.Resampling.BILINEAR)
except Exception as e: except Exception as e:
raise ParityError(f"Failed to resize image {image_path}: {e}") raise ParityError(f"Failed to resize image {image_path}: {e}")
# Konversi ke matriks float32 dan penambahan dimensi batch (1, H, W, C)
img_array = np.array(img, dtype=np.float32) img_array = np.array(img, dtype=np.float32)
img_batch = np.expand_dims(img_array, axis=0) img_batch = np.expand_dims(img_array, axis=0)
@@ -60,6 +63,7 @@ def predict_keras(model, image_batch):
Returns: Returns:
Predictions array (1, num_classes). Predictions array (1, num_classes).
""" """
# Eksekusi inferensi pada model TensorFlow/Keras tanpa log proses
predictions = model.predict(image_batch, verbose=0) predictions = model.predict(image_batch, verbose=0)
return predictions return predictions
@@ -75,6 +79,7 @@ def predict_onnx(session, image_batch):
Returns: Returns:
Predictions array (1, num_classes). Predictions array (1, num_classes).
""" """
# Eksekusi inferensi secara dinamis pada model ONNX menggunakan sesi runtime
input_name = session.get_inputs()[0].name input_name = session.get_inputs()[0].name
predictions = session.run(None, {input_name: image_batch}) predictions = session.run(None, {input_name: image_batch})
return predictions[0] return predictions[0]
@@ -94,14 +99,18 @@ def validate_image(image_path, keras_model, onnx_session, input_size, atol):
Raises: Raises:
ParityError: If predictions do not match or image cannot be processed. ParityError: If predictions do not match or image cannot be processed.
""" """
# Menyiapkan tensor gambar untuk pengujian
img_batch = preprocess_image(image_path, input_size) img_batch = preprocess_image(image_path, input_size)
# Mengekstrak matriks probabilitas dari kedua format model
keras_pred = predict_keras(keras_model, img_batch) keras_pred = predict_keras(keras_model, img_batch)
onnx_pred = predict_onnx(onnx_session, img_batch) onnx_pred = predict_onnx(onnx_session, img_batch)
# Mendapatkan indeks kelas dengan probabilitas tertinggi (Top-1)
keras_label_idx = np.argmax(keras_pred[0]) keras_label_idx = np.argmax(keras_pred[0])
onnx_label_idx = np.argmax(onnx_pred[0]) onnx_label_idx = np.argmax(onnx_pred[0])
# Validasi keselarasan keputusan klasifikasi utama
if keras_label_idx != onnx_label_idx: if keras_label_idx != onnx_label_idx:
keras_label = LABELS[keras_label_idx] keras_label = LABELS[keras_label_idx]
onnx_label = LABELS[onnx_label_idx] onnx_label = LABELS[onnx_label_idx]
@@ -110,6 +119,7 @@ def validate_image(image_path, keras_model, onnx_session, input_size, atol):
f"Keras={keras_label}, ONNX={onnx_label}" f"Keras={keras_label}, ONNX={onnx_label}"
) )
# Validasi selisih nilai desimal probabilitas menggunakan toleransi absolut
if not np.allclose(keras_pred, onnx_pred, atol=atol): if not np.allclose(keras_pred, onnx_pred, atol=atol):
max_diff = np.max(np.abs(keras_pred - onnx_pred)) max_diff = np.max(np.abs(keras_pred - onnx_pred))
raise ParityError( raise ParityError(
@@ -117,6 +127,7 @@ def validate_image(image_path, keras_model, onnx_session, input_size, atol):
f"max difference={max_diff:.6e} (atol={atol})" f"max difference={max_diff:.6e} (atol={atol})"
) )
# Pencatatan log sistem jika kedua model presisi 100%
label = LABELS[keras_label_idx] label = LABELS[keras_label_idx]
logging.info(f"PASS: {image_path} -> {label}") logging.info(f"PASS: {image_path} -> {label}")
@@ -125,6 +136,7 @@ def main():
"""Validate parity between Keras and ONNX models.""" """Validate parity between Keras and ONNX models."""
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
# Inisialisasi parser argumen untuk antarmuka CLI (Command Line Interface)
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(
description="Validate parity between Keras and ONNX models" description="Validate parity between Keras and ONNX models"
) )
@@ -161,6 +173,7 @@ def main():
args = parser.parse_args() args = parser.parse_args()
# Pengecekan eksistensi berkas model sebelum memuat memori
if not args.keras_model.exists(): if not args.keras_model.exists():
msg = f"Keras model not found at {args.keras_model}" msg = f"Keras model not found at {args.keras_model}"
logging.error(msg) logging.error(msg)
@@ -171,15 +184,18 @@ def main():
logging.error(msg) logging.error(msg)
raise FileNotFoundError(msg) raise FileNotFoundError(msg)
# Memuat model Keras (tanpa kompilasi agar lebih hemat beban komputasi)
logging.info(f"Loading Keras model from {args.keras_model}...") logging.info(f"Loading Keras model from {args.keras_model}...")
keras_model = tf.keras.models.load_model(args.keras_model, compile=False) keras_model = tf.keras.models.load_model(args.keras_model, compile=False)
# Memuat sesi ONNX dengan penyedia eksekusi CPU murni
logging.info(f"Loading ONNX model from {args.onnx_model}...") logging.info(f"Loading ONNX model from {args.onnx_model}...")
onnx_session = ort.InferenceSession( onnx_session = ort.InferenceSession(
str(args.onnx_model), str(args.onnx_model),
providers=["CPUExecutionProvider"], providers=["CPUExecutionProvider"],
) )
# Iterasi pengujian paritas (kesetaraan performa) untuk setiap gambar
logging.info(f"Validating {len(args.images)} image(s)...") logging.info(f"Validating {len(args.images)} image(s)...")
for image_path in args.images: for image_path in args.images:
try: try:
+1 -1
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@@ -2,7 +2,7 @@
# ZeaVis Edu — Root Makefile # ZeaVis Edu — Root Makefile
# #
# Orchestrates the application stack (web, api, ml) and the telemetry # Orchestrates the application stack (web, api, ml) and the telemetry
# metric pipeline (Prometheus → Ingester → Vector → ClickHouse). # metric pipeline (Prometheus → Grafana).
# #
# Telemetry commands operate on the submodule at telemetry/. # Telemetry commands operate on the submodule at telemetry/.
# ============================================================================= # =============================================================================
+4 -1
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@@ -66,7 +66,10 @@ ZeaVis Edu menggunakan **Computer Vision** sebagai asisten edukasi interaktif:
|---|---| |---|---|
| Arsitektur Model | **EfficientNetV2B0** — keseimbangan optimal antara akurasi dan efisiensi parameter | | Arsitektur Model | **EfficientNetV2B0** — keseimbangan optimal antara akurasi dan efisiensi parameter |
| Metode Pelatihan | **Transfer Learning** pada Google Colab (GPU T4) | | Metode Pelatihan | **Transfer Learning** pada Google Colab (GPU T4) |
| Sumber Dataset | Kaggle — [Corn Leaf Disease](https://www.kaggle.com/datasets/ndisan/corn-leaf-disease) | | Sumber Dataset 1 | Kaggle — [Corn Leaf Disease](https://www.kaggle.com/datasets/ndisan/corn-leaf-disease) |
| Sumber Dataset 2 | Kaggle — [Corn or Maize Leaf Disease Dataset](https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset) |
| Sumber Dataset 3 | scidb — [Dataset of Corn Leaf Diseases based on Manual Annotation and Contrast Generation Model](https://www.scidb.cn/en/detail?dataSetId=19536c73f6d74946a212719a94f53ab3) |
| Deployment | VPS dengan Docker, ONNX Runtime untuk inferensi real-time | | Deployment | VPS dengan Docker, ONNX Runtime untuk inferensi real-time |
--- ---
+5
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@@ -4,6 +4,11 @@
<meta charset="UTF-8" /> <meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>ZeaVis Edu</title> <title>ZeaVis Edu</title>
<link
rel="icon"
type="image/svg+xml"
href="data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24' fill='none' stroke='%2322C55E' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpath d='M11 20A7 7 0 0 1 9.8 6.1C15.5 5 17 4.48 19 2c1 2 2 4.18 2 8 0 5.5-4.78 10-10 10Z'/%3E%3Cpath d='M2 22l10-10'/%3E%3C/svg%3E"
/>
</head> </head>
<body> <body>
<div id="root"></div> <div id="root"></div>
+103 -34
View File
@@ -1,25 +1,42 @@
import { FormEvent, useState, useCallback } from 'react'; import { FormEvent, useState, useCallback } from "react";
import { Eye, EyeOff } from 'lucide-react'; import { Eye, EyeOff } from "lucide-react";
import { Button } from '@/components/ui/button'; import { Button } from "@/components/ui/button";
import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card'; import {
import { Input } from '@/components/ui/input'; Card,
import { Label } from '@/components/ui/label'; CardContent,
import { apiBaseUrl } from '@/lib/api-client'; CardDescription,
import { isTauri, openUrl } from '@/lib/tauri'; CardHeader,
CardTitle,
} from "@/components/ui/card";
import { Input } from "@/components/ui/input";
import { Label } from "@/components/ui/label";
import { apiBaseUrl } from "@/lib/api-client";
import { isTauri, openUrl } from "@/lib/tauri";
type AuthFormProps = { type AuthFormProps = {
mode: 'login' | 'register'; mode: "login" | "register";
isSubmitting: boolean; isSubmitting: boolean;
error: string | null; error: string | null;
googleOAuthEnabled: boolean; googleOAuthEnabled: boolean;
onSubmit: (payload: { name?: string; email: string; password: string }) => Promise<unknown>; onSubmit: (payload: {
name?: string;
email: string;
password: string;
}) => Promise<unknown>;
onFieldChange?: () => void; onFieldChange?: () => void;
}; };
export function AuthForm({ mode, isSubmitting, error, googleOAuthEnabled, onSubmit, onFieldChange }: AuthFormProps) { export function AuthForm({
const [name, setName] = useState(''); mode,
const [email, setEmail] = useState(''); isSubmitting,
const [password, setPassword] = useState(''); error,
googleOAuthEnabled,
onSubmit,
onFieldChange,
}: AuthFormProps) {
const [name, setName] = useState("");
const [email, setEmail] = useState("");
const [password, setPassword] = useState("");
const [showPassword, setShowPassword] = useState(false); const [showPassword, setShowPassword] = useState(false);
async function handleSubmit(event: FormEvent<HTMLFormElement>) { async function handleSubmit(event: FormEvent<HTMLFormElement>) {
@@ -29,7 +46,7 @@ export function AuthForm({ mode, isSubmitting, error, googleOAuthEnabled, onSubm
const handleGoogleLogin = useCallback(async (e: React.MouseEvent) => { const handleGoogleLogin = useCallback(async (e: React.MouseEvent) => {
e.preventDefault(); e.preventDefault();
const platform = isTauri() ? 'tauri' : 'web'; const platform = isTauri() ? "tauri" : "web";
const googleUrl = `${apiBaseUrl}/api/v1/auth/google?platform=${platform}`; const googleUrl = `${apiBaseUrl}/api/v1/auth/google?platform=${platform}`;
await openUrl(googleUrl); await openUrl(googleUrl);
}, []); }, []);
@@ -37,59 +54,111 @@ export function AuthForm({ mode, isSubmitting, error, googleOAuthEnabled, onSubm
return ( return (
<Card className="mx-auto w-full max-w-md bg-transparent border-none shadow-none"> <Card className="mx-auto w-full max-w-md bg-transparent border-none shadow-none">
<CardHeader className="text-center space-y-2"> <CardHeader className="text-center space-y-2">
<CardTitle className="text-2xl font-bold text-emerald-900">{mode === 'login' ? 'Masuk Akun ZeaVis Edu' : 'Buat akun ZeaVis Edu'}</CardTitle> <CardTitle className="text-2xl font-bold text-emerald-900">
{mode === "login" ? "Masuk Akun ZeaVis Edu" : "Buat akun ZeaVis Edu"}
</CardTitle>
<CardDescription className="text-sm text-emerald-800/80"> <CardDescription className="text-sm text-emerald-800/80">
{mode === 'login' {mode === "login"
? 'Masuk untuk menyimpan diagnosis dan mengikuti review pakar.' ? "Masuk untuk menyimpan diagnosis dan mengikuti review pakar."
: 'Daftar untuk menyimpan diagnosis dan mengikuti review pakar.'} : "Daftar untuk menyimpan diagnosis dan mengikuti review pakar."}
</CardDescription> </CardDescription>
</CardHeader> </CardHeader>
<CardContent> <CardContent>
<form className="space-y-4" onSubmit={handleSubmit}> <form className="space-y-4" onSubmit={handleSubmit}>
{mode === 'register' && ( {mode === "register" && (
<div className="space-y-2"> <div className="space-y-2">
<Label htmlFor="name">Nama</Label> <Label htmlFor="name">Nama</Label>
<Input id="name" value={name} onChange={(event) => { <Input
id="name"
placeholder="Masukkan nama Anda"
value={name}
onChange={(event) => {
setName(event.target.value); setName(event.target.value);
onFieldChange?.(); onFieldChange?.();
}} required /> }}
required
/>
</div> </div>
)} )}
<div className="space-y-2"> <div className="space-y-2">
<Label htmlFor="email">Email</Label> <Label htmlFor="email">Email</Label>
<Input id="email" type="email" value={email} onChange={(event) => { <Input
id="email"
type="email"
placeholder="Masukkan email Anda"
value={email}
onChange={(event) => {
setEmail(event.target.value); setEmail(event.target.value);
onFieldChange?.(); onFieldChange?.();
}} required /> }}
required
/>
</div> </div>
<div className="space-y-2"> <div className="space-y-2">
<Label htmlFor="password">Password</Label> <Label htmlFor="password">Password</Label>
<div className="relative"> <div className="relative">
<Input id="password" type={showPassword ? "text" : "password"} minLength={8} value={password} onChange={(event) => { <Input
id="password"
type={showPassword ? "text" : "password"}
placeholder="Password minimal 8 karakter"
minLength={8}
value={password}
onChange={(event) => {
setPassword(event.target.value); setPassword(event.target.value);
onFieldChange?.(); onFieldChange?.();
}} required /> }}
required
/>
<button <button
type="button" type="button"
className="absolute right-3 top-1/2 -translate-y-1/2 text-muted-foreground focus:outline-none" className="absolute right-3 top-1/2 -translate-y-1/2 text-muted-foreground focus:outline-none"
onClick={() => setShowPassword(!showPassword)} onClick={() => setShowPassword(!showPassword)}
> >
{showPassword ? <EyeOff className="h-4 w-4" /> : <Eye className="h-4 w-4" />} {showPassword ? (
<EyeOff className="h-4 w-4" />
) : (
<Eye className="h-4 w-4" />
)}
</button> </button>
</div> </div>
</div> </div>
{error && <p className="text-sm text-red-600" role="alert">{error}</p>} {error && (
<p className="text-sm text-red-600" role="alert">
{error}
</p>
)}
<Button className="w-full" type="submit" disabled={isSubmitting}> <Button className="w-full" type="submit" disabled={isSubmitting}>
{isSubmitting ? 'Memproses...' : mode === 'login' ? 'Masuk' : 'Daftar'} {isSubmitting
? "Memproses..."
: mode === "login"
? "Masuk"
: "Daftar"}
</Button> </Button>
</form> </form>
{googleOAuthEnabled && ( {googleOAuthEnabled && (
<Button className="mt-3 w-full flex items-center justify-center gap-2.5" variant="outline" onClick={handleGoogleLogin} type="button"> <Button
className="mt-3 w-full flex items-center justify-center gap-2.5"
variant="outline"
onClick={handleGoogleLogin}
type="button"
>
<svg viewBox="0 0 24 24" className="h-5 w-5" aria-hidden="true"> <svg viewBox="0 0 24 24" className="h-5 w-5" aria-hidden="true">
<path fill="#4285F4" d="M22.56 12.25c0-.78-.07-1.53-.2-2.25H12v4.26h5.92a5.06 5.06 0 0 1-2.2 3.32v2.77h3.57c2.08-1.92 3.28-4.74 3.28-8.1z" /> <path
<path fill="#34A853" d="M12 23c2.97 0 5.46-.98 7.28-2.66l-3.57-2.77c-.98.66-2.23 1.06-3.71 1.06-2.86 0-5.29-1.93-6.16-4.53H2.18v2.84C3.99 20.53 7.7 23 12 23z" /> fill="#4285F4"
<path fill="#FBBC05" d="M5.84 14.09c-.22-.66-.35-1.36-.35-2.09s.13-1.43.35-2.09V7.07H2.18C1.43 8.55 1 10.22 1 12s.43 3.45 1.18 4.93l2.85-2.22.81-.62z" /> d="M22.56 12.25c0-.78-.07-1.53-.2-2.25H12v4.26h5.92a5.06 5.06 0 0 1-2.2 3.32v2.77h3.57c2.08-1.92 3.28-4.74 3.28-8.1z"
<path fill="#EA4335" d="M12 5.38c1.62 0 3.06.56 4.21 1.64l3.15-3.15C17.45 2.09 14.97 1 12 1 7.7 1 3.99 3.47 2.18 7.07l3.66 2.84c.87-2.6 3.3-4.53 6.16-4.53z" /> />
<path
fill="#34A853"
d="M12 23c2.97 0 5.46-.98 7.28-2.66l-3.57-2.77c-.98.66-2.23 1.06-3.71 1.06-2.86 0-5.29-1.93-6.16-4.53H2.18v2.84C3.99 20.53 7.7 23 12 23z"
/>
<path
fill="#FBBC05"
d="M5.84 14.09c-.22-.66-.35-1.36-.35-2.09s.13-1.43.35-2.09V7.07H2.18C1.43 8.55 1 10.22 1 12s.43 3.45 1.18 4.93l2.85-2.22.81-.62z"
/>
<path
fill="#EA4335"
d="M12 5.38c1.62 0 3.06.56 4.21 1.64l3.15-3.15C17.45 2.09 14.97 1 12 1 7.7 1 3.99 3.47 2.18 7.07l3.66 2.84c.87-2.6 3.3-4.53 6.16-4.53z"
/>
<path fill="none" d="M1 1h22v22H1z" /> <path fill="none" d="M1 1h22v22H1z" />
</svg> </svg>
Masuk dengan Google Masuk dengan Google
+19 -1
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@@ -33,6 +33,7 @@ export function ScanPage() {
const imageRef = useRef<HTMLImageElement | null>(null); const imageRef = useRef<HTMLImageElement | null>(null);
const navigate = useNavigate(); const navigate = useNavigate();
const queryClient = useQueryClient(); const queryClient = useQueryClient();
const [isDragging, setIsDragging] = useState(false);
// Camera mode state // Camera mode state
const [useCamera, setUseCamera] = useState(false); const [useCamera, setUseCamera] = useState(false);
@@ -164,8 +165,25 @@ export function ScanPage() {
) : ( ) : (
<> <>
<div <div
className="w-full border-2 border-dashed border-green-300 rounded-md p-10 h-60 text-center cursor-pointer" className={`w-full border-2 border-dashed rounded-md p-10 h-60 text-center cursor-pointer transition-colors duration-200 ${
isDragging
? "border-blue-500 bg-blue-100"
: "border-green-300"
}`}
onClick={() => inputRef.current?.click()} onClick={() => inputRef.current?.click()}
onDragOver={(e) => {
e.preventDefault();
setIsDragging(true);
}}
onDragLeave={() => setIsDragging(false)}
onDrop={(e) => {
e.preventDefault();
setIsDragging(false);
const droppedFile = e.dataTransfer.files?.[0];
if (droppedFile) {
handleFile(droppedFile);
}
}}
> >
<Upload <Upload
className="mx-auto text-green-500 mb-3" className="mx-auto text-green-500 mb-3"