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.gitattributes

@@ -69,3 +69,5 @@ feedback/real/real9.jpg filter=lfs diff=lfs merge=lfs -text
 feedback/real/Screenshot_20260430_064350.jpg filter=lfs diff=lfs merge=lfs -text
 feedback/real/WhatsApp[[:space:]]Image[[:space:]]2026-05-12[[:space:]]at[[:space:]]14.58.23.jpeg filter=lfs diff=lfs merge=lfs -text
 feedback/real/WhatsApp[[:space:]]Image[[:space:]]2026-05-17[[:space:]]at[[:space:]]10.20.34.jpeg filter=lfs diff=lfs merge=lfs -text
+feedback/fake/ai9.png filter=lfs diff=lfs merge=lfs -text
+feedback/pending/695d9e42_Gemini_Generated_Image_[[:space:]](3).png filter=lfs diff=lfs merge=lfs -text

backend.py

@@ -157,11 +157,19 @@ def api_scan_image(file: UploadFile = File(...), username: str = Form(...)):
 
         result = resp.json()
         prediction = result.get("prediction", "REAL")
-        confidence = result.get("confidence", 0.0)
+        confidence_str = result.get("confidence", "0.0%")
+        
+        try:
+            if isinstance(confidence_str, str):
+                confidence_val = float(confidence_str.replace("%", "").strip()) / 100.0
+            else:
+                confidence_val = float(confidence_str)
+        except Exception:
+            confidence_val = 0.85 # fallback
 
         is_ai = prediction.upper() in ("AI", "FAKE")
         source = "Pollinations AI (Stable Diffusion)" if is_ai else "Kamera/Foto Digital Asli"
-        accuracy = round(confidence * 100, 1)
+        accuracy = round(confidence_val * 100, 1)
 
         feedback_path = f"{PENDING_DIR}/{uid}_{safe_name}"
         save_compressed_image(temp_path, feedback_path)
@@ -177,7 +185,9 @@ def api_scan_image(file: UploadFile = File(...), username: str = Form(...)):
         is_outlier = 1 if similarity < 0.88 else 0
 
         # Detect low-light and monochrome conditions
-        is_dark, is_grayscale, avg_brightness = analyze_image_conditions(temp_path)
+        is_dark, is_grayscale_local, avg_brightness = analyze_image_conditions(temp_path)
+        is_monochrome_detected = bool(result.get("is_monochrome_detected", False))
+        is_grayscale = is_monochrome_detected
 
         # Check for Trap image (Solusi 3)
         is_trap = 1 if "trap" in file.filename.lower() else 0
@@ -229,7 +239,9 @@ def api_scan_image(file: UploadFile = File(...), username: str = Form(...)):
             "trust_score": trust_score,
             "is_dark": is_dark,
             "is_grayscale": is_grayscale,
-            "avg_brightness": avg_brightness
+            "is_monochrome_detected": is_monochrome_detected,
+            "avg_brightness": avg_brightness,
+            "forensic_analysis_logs": result.get("forensic_analysis_logs", {})
         }
     finally:
         if os.path.exists(temp_path): os.remove(temp_path)
@@ -288,9 +300,18 @@ async def api_batch_scan(files: list[UploadFile] = File(...), username: str = Fo
 
             result = resp.json()
             prediction = result.get("prediction", "REAL")
-            confidence = result.get("confidence", 0.0)
+            confidence_str = result.get("confidence", "0.0%")
+            
+            try:
+                if isinstance(confidence_str, str):
+                    confidence_val = float(confidence_str.replace("%", "").strip()) / 100.0
+                else:
+                    confidence_val = float(confidence_str)
+            except Exception:
+                confidence_val = 0.85 # fallback
+
             prediction_label = "AI" if prediction.upper() in ("AI", "FAKE") else "REAL"
-            confidence_pct = round(confidence * 100, 1)
+            confidence_pct = round(confidence_val * 100, 1)
 
             is_mismatch = 0
             if folder_label:
@@ -489,6 +510,26 @@ def api_correction_single(data: dict):
         "new_trust": new_trust
     }
 
+@app.post("/save-feedback")
+async def save_feedback_direct(file: UploadFile = File(...), correct_label: str = Form(...)):
+    # Standardize correct label to folder target
+    folder = "real" if correct_label.lower() in ("real", "true", "asli") else "fake"
+    target_dir = os.path.join(FEEDBACK_DIR, folder)
+    os.makedirs(target_dir, exist_ok=True)
+    
+    safe_name = file.filename.replace("\\", "/").split("/")[-1]
+    target_path = os.path.join(target_dir, safe_name)
+    
+    # Save uploaded file content directly
+    contents = await file.read()
+    with open(target_path, "wb") as f:
+        f.write(contents)
+        
+    return {
+        "status": "saved",
+        "path": f"feedback/{folder}/{safe_name}"
+    }
+
 @app.get("/api/history/{username}")
 def api_get_history(username: str):
     return {"history": database.get_user_history(username)}

index.html

@@ -110,6 +110,9 @@
                     </div>
                     <button class="btn-scan" style="width: 100%; margin-top: 15px;" onclick="scanLandingFile()">🔍 PINDAI GAMBAR</button>
                 </div>
+                <div id="landing-warning-banner-monokrom" style="display: none; background: rgba(230,126,34,0.15); border: 1px dashed rgba(230,126,34,0.3); border-radius: 8px; padding: 12px; margin-top: 15px; font-size: 12px; color: #f39c12; line-height: 1.5; text-align: left;">
+                    <b>⚠️ Peringatan Citra Monokrom:</b> Gambar hitam-putih terdeteksi. Kehilangan informasi warna RGB dapat menurunkan keakuratan klasifikasi model AI. Disarankan menggunakan foto penuh warna.
+                </div>
 
                 <!-- Result Box for Landing Page -->
                 <div id="landing-result-image" class="result-box hidden" style="margin-top: 20px;"></div>
@@ -226,6 +229,9 @@
                     </div>
                     <button class="btn-scan" onclick="scanFile()">🔍 SCAN GAMBAR</button>
                 </div>
+                <div id="warning-banner-monokrom" style="display: none; background: rgba(230,126,34,0.15); border: 1px dashed rgba(230,126,34,0.3); border-radius: 8px; padding: 12px; margin-top: 15px; font-size: 12px; color: #f39c12; line-height: 1.5; text-align: left;">
+                    <b>⚠️ Peringatan Citra Monokrom:</b> Gambar hitam-putih terdeteksi. Kehilangan informasi warna RGB dapat menurunkan keakuratan klasifikasi model AI. Disarankan menggunakan foto penuh warna.
+                </div>
                 <div id="result-image" class="result-box hidden"></div>
             </section>
 

script.js

@@ -323,6 +323,18 @@ async function simulateForensicAnalysis(resultBox, fetchPromise) {
     container.scrollTop = container.scrollHeight;
 }
 
+function toggleForensicLogs(btn) {
+    const content = btn.nextElementSibling;
+    const arrow = btn.querySelector("span:last-child");
+    if (content.classList.contains("hidden")) {
+        content.classList.remove("hidden");
+        arrow.innerText = "▲";
+    } else {
+        content.classList.add("hidden");
+        arrow.innerText = "▼";
+    }
+}
+
 async function scanFile() {
     const fileInput = document.getElementById("input-image");
     const resultBox = document.getElementById("result-image");
@@ -353,6 +365,16 @@ async function scanFile() {
         const res = await fetchPromise;
         const data = await res.json();
 
+        // CEK OTOMATIS GAMBAR HITAM PUTIH UNTUK TAMPILKAN BANNER ALERT
+        const warningBanner = document.getElementById("warning-banner-monokrom");
+        if (warningBanner) {
+            if (data.is_monochrome_detected === true) {
+                warningBanner.style.display = "block";
+            } else {
+                warningBanner.style.display = "none";
+            }
+        }
+
         if (res.ok) {
             if (currentUser === "__guest__") {
                 let count = parseInt(localStorage.getItem("guest_scan_count") || "0");
@@ -365,6 +387,26 @@ async function scanFile() {
             const statusText = data.is_ai ? "⚠️ GAMBAR PALSU (AI)" : "✅ GAMBAR ASLI (REAL)";
             const statusColor = data.is_ai ? "var(--danger)" : "var(--success)";
 
+            let logsHtml = '';
+            if (data.forensic_analysis_logs && Object.keys(data.forensic_analysis_logs).length > 0) {
+                logsHtml = `
+                <div class="forensic-logs-detail" style="margin-top: 15px; margin-bottom: 15px; text-align: left;">
+                    <button class="btn-refresh" style="width: 100%; text-align: left; padding: 10px 15px; background: rgba(0,0,0,0.3); border: 1px solid rgba(255,215,0,0.25); border-radius: 8px; color: var(--yellow-main); cursor: pointer; font-size: 13px; display: flex; justify-content: space-between; align-items: center; margin-bottom: 0;" onclick="toggleForensicLogs(this)">
+                        <span>🔍 Lihat Log Detil Forensik (12 Langkah)</span>
+                        <span style="font-size:10px">▼</span>
+                    </button>
+                    <div class="logs-content hidden" style="background: rgba(0,0,0,0.5); border: 1px solid rgba(255,215,0,0.15); border-top: none; border-radius: 0 0 8px 8px; padding: 15px; font-family: monospace; font-size: 12px; color: #2ed573; line-height: 1.6; max-height: 250px; overflow-y: auto;">
+                `;
+                for (let i = 1; i <= 12; i++) {
+                    const stepText = data.forensic_analysis_logs[`step_${i}`] || `[Step ${i}/12] Analisis selesai.`;
+                    logsHtml += `<div style="margin-bottom: 6px; border-bottom: 1px dashed rgba(255,255,255,0.05); padding-bottom: 4px;">${stepText}</div>`;
+                }
+                logsHtml += `
+                    </div>
+                </div>
+                `;
+            }
+
             resultBox.className = `result-box ${statusClass}`;
             resultBox.innerHTML = `
                 <div class="result-title" style="color: ${statusColor}">
@@ -399,12 +441,14 @@ async function scanFile() {
                         <b>⚠️ Rekomendasi Kondisi Deteksi:</b><br/>
                         ${data.is_dark ? '• Cahaya terdeteksi rendah (kecerahan rata-rata: ' + data.avg_brightness + '/255). Hal ini memicu noise sensor kamera yang dapat mengganggu keakuratan forensik AI.<br/>' : ''}
                         ${data.is_grayscale ? '• Gambar monokrom/hitam-putih terdeteksi. Kehilangan informasi kromatik (saluran warna RGB) secara drastis dapat menurunkan performa klasifikasi model AI.<br/>' : ''}
-                        <i style="display:block;margin-top:6px;color:rgba(255,255,255,0.7)">Disarankan untuk melakukan scan ulang menggunakan foto dengan pencahayaan cukup dan penuh warna (Full RGB).</i>
+                        <i style="display:block;margin-top:6px;color:rgba(255,255,255,0.7)">Disarankan untuk melakukan scan ulang menggunakan foto dengan pencahayaan cukup and penuh warna (Full RGB).</i>
                     </div>
                     ` 
                     : ''
                 }
 
+                ${logsHtml}
+
                 <div class="details-grid">
                     <div class="detail-item">
                         <div class="detail-label">Nama File</div>
@@ -1010,6 +1054,16 @@ async function scanLandingFile() {
         const res = await fetchPromise;
         const data = await res.json();
 
+        // CEK OTOMATIS GAMBAR HITAM PUTIH UNTUK TAMPILKAN BANNER ALERT
+        const warningBanner = document.getElementById("landing-warning-banner-monokrom");
+        if (warningBanner) {
+            if (data.is_monochrome_detected === true) {
+                warningBanner.style.display = "block";
+            } else {
+                warningBanner.style.display = "none";
+            }
+        }
+
         if (res.ok) {
             let count = parseInt(localStorage.getItem("guest_scan_count") || "0");
             count++;
@@ -1020,6 +1074,26 @@ async function scanLandingFile() {
             const statusText = data.is_ai ? "⚠️ GAMBAR PALSU (AI)" : "✅ GAMBAR ASLI (REAL)";
             const statusColor = data.is_ai ? "var(--danger)" : "var(--success)";
 
+            let logsHtml = '';
+            if (data.forensic_analysis_logs && Object.keys(data.forensic_analysis_logs).length > 0) {
+                logsHtml = `
+                <div class="forensic-logs-detail" style="margin-top: 15px; margin-bottom: 15px; text-align: left;">
+                    <button class="btn-refresh" style="width: 100%; text-align: left; padding: 10px 15px; background: rgba(0,0,0,0.3); border: 1px solid rgba(255,215,0,0.25); border-radius: 8px; color: var(--yellow-main); cursor: pointer; font-size: 13px; display: flex; justify-content: space-between; align-items: center; margin-bottom: 0;" onclick="toggleForensicLogs(this)">
+                        <span>🔍 Lihat Log Detil Forensik (12 Langkah)</span>
+                        <span style="font-size:10px">▼</span>
+                    </button>
+                    <div class="logs-content hidden" style="background: rgba(0,0,0,0.5); border: 1px solid rgba(255,215,0,0.15); border-top: none; border-radius: 0 0 8px 8px; padding: 15px; font-family: monospace; font-size: 12px; color: #2ed573; line-height: 1.6; max-height: 250px; overflow-y: auto;">
+                `;
+                for (let i = 1; i <= 12; i++) {
+                    const stepText = data.forensic_analysis_logs[`step_${i}`] || `[Step ${i}/12] Analisis selesai.`;
+                    logsHtml += `<div style="margin-bottom: 6px; border-bottom: 1px dashed rgba(255,255,255,0.05); padding-bottom: 4px;">${stepText}</div>`;
+                }
+                logsHtml += `
+                    </div>
+                </div>
+                `;
+            }
+
             resultBox.className = `result-box ${statusClass}`;
             resultBox.innerHTML = `
                 <div class="result-title" style="color: ${statusColor}">
@@ -1060,6 +1134,8 @@ async function scanLandingFile() {
                     : ''
                 }
 
+                ${logsHtml}
+
                 <div class="details-grid">
                     <div class="detail-item">
                         <div class="detail-label">Nama File</div>