Update app.py

app.py

@@ -1,103 +1,80 @@
 import gradio as gr
 import torch
 import numpy as np
-from PIL import Image
+from collections import defaultdict
 from transformers import AutoImageProcessor, AutoModel
-import os
-
-# =========================
-# Load model pretrained
-# =========================
-processor = AutoImageProcessor.from_pretrained(
-    "google/vit-base-patch16-224"
-)
-model = AutoModel.from_pretrained(
-    "google/vit-base-patch16-224"
-)
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Load model pretrained (visual embedding)
+processor = AutoImageProcessor.from_pretrained("facebook/dino-vits16")
+model = AutoModel.from_pretrained("facebook/dino-vits16")
 model.eval()
 
-# =========================
-# Ekstraksi fitur
-# =========================
-def get_embedding(image):
+# Database: label -> list of features
+feature_db = defaultdict(list)
+
+def extract_feature(image):
     inputs = processor(images=image, return_tensors="pt")
     with torch.no_grad():
         outputs = model(**inputs)
-    embedding = outputs.last_hidden_state.mean(dim=1)
-    return embedding.squeeze().numpy()
-
-# =========================
-# Cosine similarity
-# =========================
-def cosine_similarity(a, b):
-    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
-
-# =========================
-# Fungsi utama
-# =========================
-def unsupervised_image_similarity(sample_path, test_path):
-    if sample_path is None or test_path is None:
-        return "❌ Silakan upload gambar sampling dan gambar uji."
-
-    # Ambil nama file sampling
-    sample_name = os.path.basename(sample_path)
-
-    # Buka gambar
-    img_sample = Image.open(sample_path).convert("RGB")
-    img_test = Image.open(test_path).convert("RGB")
-
-    # Ekstraksi embedding
-    emb1 = get_embedding(img_sample)
-    emb2 = get_embedding(img_test)
-
-    similarity = cosine_similarity(emb1, emb2)
-
-    # Threshold
-    threshold = 0.75
-
-    if similarity >= threshold:
-        status = (
-            f"✅ GAMBAR UJI MEMILIKI KEMIRIPAN\n"
-            f"dengan SAMPLING: **{sample_name}**"
-        )
-    else:
-        status = (
-            "❌ GAMBAR UJI TIDAK MEMILIKI KEMIRIPAN\n"
-            "dengan sampling yang diinput"
-        )
-
-    hasil = f"""
-HASIL UNSUPERVISED LEARNING
-
-Nama sampling  : {sample_name}
-Nilai kemiripan: {similarity:.2f}
-
-Keputusan:
-{status}
-
-Catatan:
-- Tidak menggunakan dataset
-- Tidak menggunakan label
-- Tidak melakukan training ulang
-- Pendekatan unsupervised berbasis similarity
-"""
+    return outputs.last_hidden_state.mean(dim=1).numpy()
+
+def tambah_sampling(image, label):
+    if image is None or label.strip() == "":
+        return "❌ Gambar dan label wajib diisi"
+
+    feat = extract_feature(image)
+    feature_db[label].append(feat)
+
+    return f"✅ Sampling ditambahkan untuk label '{label}' (total: {len(feature_db[label])})"
+
+def tebak_gambar(image):
+    if image is None:
+        return "❌ Upload gambar terlebih dahulu"
+
+    if len(feature_db) == 0:
+        return "❌ Belum ada data sampling"
+
+    test_feat = extract_feature(image)
+
+    label_scores = {}
+    for label, feats in feature_db.items():
+        sims = cosine_similarity(test_feat, np.vstack(feats))
+        label_scores[label] = sims.mean()  # rata-rata kemiripan
+
+    # Ambil label dengan skor tertinggi
+    pred_label = max(label_scores, key=label_scores.get)
+    skor = label_scores[pred_label]
+
+    hasil = "📌 HASIL PREDIKSI (BERDASARKAN KEMIRIPAN)\n\n"
+    for lbl, sc in sorted(label_scores.items(), key=lambda x: x[1], reverse=True):
+        hasil += f"- {lbl}: {sc:.2f}\n"
+
+    hasil += f"\n✅ Gambar PALING MIRIP dengan: **{pred_label}**"
+
     return hasil
 
-# =========================
-# UI Gradio
-# =========================
-app = gr.Interface(
-    fn=unsupervised_image_similarity,
-    inputs=[
-        gr.Image(type="filepath", label="Gambar Sampling"),
-        gr.Image(type="filepath", label="Gambar Uji")
-    ],
-    outputs=gr.Textbox(label="Hasil Analisis"),
-    title="🧠 Unsupervised Image Similarity dengan Nama Sampling",
-    description=(
-        "Aplikasi unsupervised learning untuk mengecek apakah gambar uji "
-        "memiliki kemiripan dengan gambar sampling tertentu."
+with gr.Blocks() as app:
+    gr.Markdown("## 🧠 Tebak Gambar Supervised (Similarity-Based)")
+    gr.Markdown(
+        "Upload gambar sampling dengan label, lalu upload gambar baru. "
+        "AI menentukan label berdasarkan kemiripan visual, bukan gambar yang sama persis."
     )
-)
+
+    with gr.Tab("1️⃣ Tambah Data Sampling"):
+        img1 = gr.Image(type="pil", label="Gambar Sampling")
+        lbl = gr.Textbox(label="Label")
+        out1 = gr.Textbox(label="Status")
+        btn1 = gr.Button("Tambah")
+
+        btn1.click(tambah_sampling, [img1, lbl], out1)
+
+    with gr.Tab("2️⃣ Tebak Gambar"):
+        img2 = gr.Image(type="pil", label="Gambar Uji")
+        out2 = gr.Textbox(label="Hasil")
+        btn2 = gr.Button("Tebak")
+
+        btn2.click(tebak_gambar, img2, out2)
 
 app.launch()
+