batch review update

demo_app.py

@@ -466,7 +466,13 @@ def generate_batch_report(data, uploaded_files_map=None):
 
 
 # --- Tabs ---
-tab1, tab2, tab3, tab4 = st.tabs(["Single Analysis", "Batch Processing", "Similarity Search", "History Vault"])
+tab1, tab2, tab3, tab4, tab5 = st.tabs([
+    "Single Analysis", 
+    "Batch Processing", 
+    "Similarity Search", 
+    "History Vault", 
+    "Batch Reviewer"
+])
 
 # --- Tab 1: Single Analysis ---
 with tab1:
@@ -947,3 +953,93 @@ with tab4:
             st.error(f"Vault Connection Failed: {res.text}")
     except Exception as e:
         st.error(f"Audit System Error: {str(e)}")
+
+# --- Tab 5: Batch Reviewer ---
+with tab5:
+    st.subheader("📦 Local Batch Reviewer")
+    st.caption("Provide a local directory path to review the AI Data Contract and evidence.")
+
+    # 1. Path Input
+    batch_path = st.text_input(
+        "Enter Batch Folder Path:", 
+        placeholder="e.g., batch_outputs/BATCH_2646CB27",
+        help="The folder should contain 'manifest.json' and a 'raw' subfolder."
+    )
+
+    if batch_path:
+        manifest_path = os.path.join(batch_path, "manifest.json")
+        raw_dir = os.path.join(batch_path, "raw")
+
+        # 2. Validation
+        if not os.path.exists(manifest_path):
+            st.error(f"❌ Could not find `manifest.json` at: `{manifest_path}`")
+        elif not os.path.exists(raw_dir):
+            st.error(f"❌ Could not find `raw` folder at: `{raw_dir}`")
+        else:
+            # 3. Load the Contract
+            try:
+                with open(manifest_path, 'r') as f:
+                    manifest = json.load(f)
+
+                # --- Batch Header: Metadata Audit ---
+                with st.container(border=True):
+                    c1, c2, c3 = st.columns(3)
+                    with c1:
+                        st.metric("Batch ID", manifest['job_id'])
+                    with c2:
+                        ctx = manifest.get('source_context', {})
+                        st.write(f"**Venue:** {ctx.get('estate', 'N/A')}")
+                        st.write(f"**Block:** {ctx.get('block', 'B12')}")
+                    with c3:
+                        eng = manifest.get('engine', {})
+                        st.write(f"**AI Engine:** {eng.get('name')} ({eng.get('type')})")
+                        st.write(f"**Threshold:** {eng.get('threshold')}")
+
+                st.divider()
+
+                # --- Inventory Review ---
+                st.write("### 📂 Production Inventory")
+                for item in manifest['inventory']:
+                    fname = item['filename']
+                    img_full_path = os.path.join(raw_dir, fname)
+
+                    if os.path.exists(img_full_path):
+                        with st.expander(f"🖼️ {fname}", expanded=False):
+                            img = Image.open(img_full_path).convert("RGB")
+                            width, height = img.size
+                            
+                            # --- Coordinate Remapping Engine ---
+                            # We use 'norm_box' to remain resolution-agnostic for the subscriber
+                            remapped_detections = []
+                            for d in item['detections']:
+                                nx1, ny1, nx2, ny2 = d['norm_box']
+                                remapped_detections.append({
+                                    **d,
+                                    # Map ratios back to absolute pixels of the loaded image
+                                    "box": [nx1 * width, ny1 * height, nx2 * width, ny2 * height]
+                                })
+                            
+                            # --- Side-by-Side Review ---
+                            v_col1, v_col2 = st.columns([2, 1])
+                            with v_col1:
+                                # Reuse high-performance interactive viewer
+                                display_interactive_results(img, remapped_detections, key=f"rev_{item['image_id']}")
+                            
+                            with v_col2:
+                                st.write("#### 📡 Subscriber Payload")
+                                st.info("Clean metadata ready for hand-off to ERP or Vectorization.")
+                                # Extract non-geometric business data
+                                payload = [{
+                                    "id": det['bunch_id'],
+                                    "grade": det['class'],
+                                    "score": det['confidence'],
+                                    "alert": det['is_health_alert']
+                                } for det in remapped_detections]
+                                st.json(payload)
+                                
+                                if st.button(f"🚀 Vectorize Image {item['image_id']}", key=f"btn_{item['image_id']}"):
+                                    st.toast(f"Broadcasting data for {fname} to remote subscribers...")
+                    else:
+                        st.warning(f"⚠️ Image missing from /raw folder: `{fname}`")
+            except Exception as e:
+                st.error(f"Failed to load batch: {e}")