Update app.py

app.py

@@ -7,7 +7,7 @@ ROCKIT Vision Intelligence — Hugging Face Space
 GPU-accelerated multimodal search engine.
   - Embedding: Qwen3-VL-Embedding (GPU) / CLIP (CPU)
   - Search:    CAGRA (hipVS) -> PyTorch -> NumPy
-  - UI:        Premium Gradio Demo
+  - UI:        Premium Gradio Demo (Gradio >= 4.26, < 5.0)
 """
 
 import logging
@@ -35,7 +35,7 @@ from ingest import (
 from search import search_images, search_videos
 import seed_data
 
-# ── Helpers ──────────────────────────────────────────────────────────────────
+# ── Helpers ───────────────────────────────────────────────────────────────────
 
 def get_system_info(project: str = DEFAULT_PROJECT) -> str:
     img_store = get_store(project, "image_index")
@@ -55,15 +55,17 @@ def get_system_info(project: str = DEFAULT_PROJECT) -> str:
         f"| Video Frames | {vid_store.count} | {('VRAM (Hot)' if vid_store.in_vram else 'NVMe (Cold)')} |",
     ])
 
+
 def get_projects_list() -> list[str]:
     projects = list_projects()
     if DEFAULT_PROJECT not in projects:
         projects.insert(0, DEFAULT_PROJECT)
     return projects
 
-# ── Callbacks ────────────────────────────────────────────────────────────────
+# ── Callbacks ─────────────────────────────────────────────────────────────────
 
 def handle_image_upload(files, project, progress=gr.Progress()):
+    """Embed and index uploaded images one by one."""
     if not files:
         return "No files uploaded.", get_system_info(project)
     results = []
@@ -73,7 +75,9 @@ def handle_image_upload(files, project, progress=gr.Progress()):
         results.append(msg)
     return "\n".join(results), get_system_info(project)
 
+
 def handle_video_upload(files, project, progress=gr.Progress()):
+    """Extract frames and index uploaded videos."""
     if not files:
         return "No files uploaded.", get_system_info(project)
     results = []
@@ -82,25 +86,34 @@ def handle_video_upload(files, project, progress=gr.Progress()):
         results.append(msg)
     return "\n".join(results), get_system_info(project)
 
+
 def handle_batch_ingest(project, progress=gr.Progress()):
+    """Re-index all images and videos from the project's data folder."""
     img_count, img_log = ingest_images(project=project, progress_callback=progress)
     vid_count, vid_log = ingest_videos(project=project, progress_callback=progress)
     log = (
         f"=== Batch Ingest Results ===\n\n"
-        f"Successfully indexed {img_count} images and {vid_count} video frames into project '{project}'."
+        f"Successfully indexed {img_count} images and {vid_count} video frames "
+        f"into project '{project}'."
     )
     return log, get_system_info(project)
 
+
 def handle_seed(project, progress=gr.Progress()):
+    """Download and seed demo data for the selected project."""
     count, log = seed_data.run(project=project, progress_callback=progress)
     return log, get_system_info(project)
 
+
 def handle_clear(project):
+    """Purge all vector indexes for the selected project."""
     get_store(project, "image_index").clear()
     get_store(project, "video_index").clear()
     return f"All indexes cleared for project '{project}'.", get_system_info(project)
 
+
 def handle_search(query, mode, top_k, project):
+    """Run semantic search and return AI summary + gallery items."""
     if not query.strip():
         return "Please enter a search query.", [], ""
 
@@ -114,10 +127,9 @@ def handle_search(query, mode, top_k, project):
             score = r.get("score", 0)
             if path and os.path.exists(path):
                 gallery_items.append((path, f"{name} (Score: {score:.3f})"))
-        
         return summary, gallery_items, result["store_info"]
 
-    else:
+    else:  # Video Intelligence
         result = search_videos(query, project=project, top_k=int(top_k))
         summary = result["llm_summary"]
         gallery_items = []
@@ -128,10 +140,11 @@ def handle_search(query, mode, top_k, project):
             score = m.get("score", 0)
             if path and os.path.exists(path):
                 gallery_items.append((path, f"{name} @ {time_range} (Score: {score:.3f})"))
-        
         return summary, gallery_items, result["store_info"]
 
+
 def handle_create_project(name):
+    """Create a new named project workspace."""
     if not name or not name.strip():
         return "Enter a project name.", gr.update()
     name = name.strip().lower().replace(" ", "-")
@@ -139,24 +152,29 @@ def handle_create_project(name):
     get_project_dir(name)
     return f"Project '{name}' created.", gr.update(choices=get_projects_list(), value=name)
 
-# ── CSS ──────────────────────────────────────────────────────────────────────
+
+def refresh_projects():
+    """Return updated dropdown choices."""
+    return gr.update(choices=get_projects_list())
+
+# ── CSS ───────────────────────────────────────────────────────────────────────
 
 CSS = """
 @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;600;700;800&display=swap');
 
 body { font-family: 'Inter', sans-serif !important; }
 
-.gradio-container { 
-    max-width: 1300px !important; 
-    margin: 0 auto !important; 
+.gradio-container {
+    max-width: 1300px !important;
+    margin: 0 auto !important;
     background-color: #050505 !important;
 }
 
 .main-header {
     text-align: center;
     background: linear-gradient(135deg, #0f0f1b 0%, #1a1a2e 100%);
-    padding: 3rem 2rem; 
-    border-radius: 24px; 
+    padding: 3rem 2rem;
+    border-radius: 24px;
     margin-bottom: 2rem;
     border: 1px solid rgba(255,255,255,0.05);
     box-shadow: 0 10px 30px rgba(0,0,0,0.5);
@@ -173,16 +191,16 @@ body { font-family: 'Inter', sans-serif !important; }
 
 .main-header h1 {
     background: linear-gradient(90deg, #e94560, #a033ff, #4cc9f0);
-    -webkit-background-clip: text; 
+    -webkit-background-clip: text;
     -webkit-text-fill-color: transparent;
-    font-size: 3.2rem !important; 
+    font-size: 3.2rem !important;
     font-weight: 800 !important;
     margin: 0;
     letter-spacing: -1px;
 }
 
-.main-header p.subtitle { 
-    color: #94a3b8; 
+.main-header p.subtitle {
+    color: #94a3b8;
     font-size: 1.1rem;
     margin-top: 0.5rem;
 }
@@ -194,9 +212,9 @@ body { font-family: 'Inter', sans-serif !important; }
     padding: 1rem !important;
 }
 
-#search-btn { 
-    background: linear-gradient(135deg, #e94560 0%, #533483 100%) !important; 
-    border: none !important; 
+#search-btn {
+    background: linear-gradient(135deg, #e94560 0%, #533483 100%) !important;
+    border: none !important;
     font-weight: 700 !important;
     color: white !important;
     transition: all 0.3s ease;
@@ -222,31 +240,47 @@ body { font-family: 'Inter', sans-serif !important; }
 footer { display: none !important; }
 """
 
-# ── Build UI ─────────────────────────────────────────────────────────────────
+# ── Build UI ──────────────────────────────────────────────────────────────────
 
 def build_ui():
     logo_path = "assests/rockit_logo.png"
     arch_path = "assests/Architecture.svg"
     flow_path = "assests/data_flow.svg"
-    gpu_path = "assests/gpu_compute_tiers.svg"
+    gpu_path  = "assests/gpu_compute_tiers.svg"
 
     with gr.Blocks(
         title="ROCKIT Vision Intelligence",
-        theme=gr.themes.Default(
-            primary_hue="rose", 
-            secondary_hue="indigo", 
+        # FIX: gr.themes.Default() was renamed; use gr.themes.Base() or a
+        # named preset.  Soft() ships with Gradio 4 and takes the same hue
+        # kwargs.
+        theme=gr.themes.Soft(
+            primary_hue="rose",
+            secondary_hue="indigo",
             neutral_hue="slate",
         ),
         css=CSS,
     ) as app:
 
-        with gr.Div(elem_classes="main-header"):
+        # ── Header ────────────────────────────────────────────────────────────
+        with gr.Column(elem_classes="main-header"):
             if os.path.exists(logo_path):
-                gr.Image(logo_path, show_label=False, container=False, width=100, elem_classes="logo-container")
+                gr.Image(
+                    logo_path,
+                    show_label=False,
+                    container=False,
+                    width=100,
+                    elem_classes="logo-container",
+                )
             gr.HTML("<h1>ROCKIT Vision Intelligence</h1>")
-            gr.Markdown("GPU-Accelerated Multimodal Search Platform", elem_classes="subtitle")
+            gr.Markdown(
+                "GPU-Accelerated Multimodal Search Platform",
+                elem_classes="subtitle",
+            )
 
+        # ── Main layout ───────────────────────────────────────────────────────
         with gr.Row():
+
+            # Left sidebar
             with gr.Column(scale=3):
                 with gr.Group(elem_classes="card"):
                     gr.Markdown("### 🗂️ Project Selection")
@@ -258,85 +292,132 @@ def build_ui():
                             scale=4,
                             interactive=True,
                         )
+                        # FIX: outputs must be a list, not a bare component
                         refresh_btn = gr.Button("🔄", scale=1)
-                    
+
                     with gr.Accordion("Create New Project", open=False):
-                        new_project_name = gr.Textbox(label="Project ID", placeholder="e.g. security-cam")
+                        new_project_name = gr.Textbox(
+                            label="Project ID",
+                            placeholder="e.g. security-cam",
+                        )
                         create_btn = gr.Button("Initialize Project", variant="secondary")
                         create_status = gr.Markdown()
 
-                with gr.Group(elem_classes="card", visible=True):
+                # FIX: gr.Group does not accept visible= in Gradio 4 — removed
+                with gr.Group(elem_classes="card"):
                     gr.Markdown("### ⚙️ System Status")
                     system_info = gr.Markdown(value=get_system_info())
 
+            # Right content area
             with gr.Column(scale=7):
                 with gr.Tabs():
-                    
-                    # ── Tab 1: Search ──────────────────────────────────────────
+
+                    # ── Tab 1: Search ─────────────────────────────────────────
                     with gr.Tab("🔍 Search"):
                         with gr.Group(elem_classes="card"):
                             with gr.Row():
                                 with gr.Column(scale=4):
                                     query_input = gr.Textbox(
                                         label="Natural Language Query",
-                                        placeholder='Try "a cat sitting on a laptop" or "someone running in a park"',
+                                        placeholder=(
+                                            'Try "a cat sitting on a laptop" '
+                                            'or "someone running in a park"'
+                                        ),
                                         lines=2,
                                     )
                                 with gr.Column(scale=1):
-                                    search_mode = gr.Radio(["Image Search", "Video Intelligence"], value="Image Search", label="Search Mode")
-                                    top_k = gr.Slider(1, 50, value=12, step=1, label="Results Count")
-                            
-                            search_btn = gr.Button("Execute Semantic Search", variant="primary", elem_id="search-btn", size="lg")
-                        
+                                    search_mode = gr.Radio(
+                                        ["Image Search", "Video Intelligence"],
+                                        value="Image Search",
+                                        label="Search Mode",
+                                    )
+                                    top_k = gr.Slider(
+                                        1, 50, value=12, step=1,
+                                        label="Results Count",
+                                    )
+
+                            search_btn = gr.Button(
+                                "Execute Semantic Search",
+                                variant="primary",
+                                elem_id="search-btn",
+                                size="lg",
+                            )
+
                         gr.Markdown("### 🤖 AI Interpretation")
-                        search_summary = gr.Markdown("*Results will appear here...*", elem_classes="card")
-                        
+                        search_summary = gr.Markdown(
+                            "*Results will appear here...*",
+                            elem_classes="card",
+                        )
+
                         gr.Markdown("### 🖼️ Visual Matches")
                         result_gallery = gr.Gallery(
-                            label="Retrieved Media", 
-                            columns=[3, 4], 
-                            rows=[2], 
-                            object_fit="contain", 
+                            label="Retrieved Media",
+                            # FIX: columns / rows must be plain int, not list
+                            columns=4,
+                            rows=2,
+                            object_fit="contain",
                             height="auto",
-                            elem_classes="gallery-container"
+                            elem_classes="gallery-container",
                         )
-                        
+
                         with gr.Accordion("Technical Details", open=False):
-                            store_info = gr.Textbox(label="Vector Store Engine", interactive=False)
+                            store_info = gr.Textbox(
+                                label="Vector Store Engine",
+                                interactive=False,
+                            )
 
-                    # ── Tab 2: Upload ──────────────────────────────────────────
+                    # ── Tab 2: Ingest Media ───────────────────────────────────
                     with gr.Tab("📤 Ingest Media"):
                         with gr.Row():
                             with gr.Column():
                                 with gr.Group(elem_classes="card"):
                                     gr.Markdown("#### 🖼️ Image Ingestion")
-                                    img_upload = gr.File(label="Select Images", file_types=["image"], file_count="multiple")
+                                    img_upload = gr.File(
+                                        label="Select Images",
+                                        # FIX: Gradio 4 requires MIME types,
+                                        # not bare category strings
+                                        file_types=["image/*"],
+                                        file_count="multiple",
+                                    )
                                     img_btn = gr.Button("Embed & Index Images")
-                                    img_log = gr.Textbox(label="Status", lines=4, interactive=False)
-                            
+                                    img_log = gr.Textbox(
+                                        label="Status",
+                                        lines=4,
+                                        interactive=False,
+                                    )
+
                             with gr.Column():
                                 with gr.Group(elem_classes="card"):
                                     gr.Markdown("#### 🎥 Video Intelligence")
-                                    vid_upload = gr.File(label="Select Videos", file_types=["video"], file_count="multiple")
+                                    vid_upload = gr.File(
+                                        label="Select Videos",
+                                        # FIX: same MIME-type correction
+                                        file_types=["video/*"],
+                                        file_count="multiple",
+                                    )
                                     vid_btn = gr.Button("Extract & Index Frames")
-                                    vid_log = gr.Textbox(label="Status", lines=4, interactive=False)
-                        
+                                    vid_log = gr.Textbox(
+                                        label="Status",
+                                        lines=4,
+                                        interactive=False,
+                                    )
+
                         with gr.Group(elem_classes="card"):
                             gr.Markdown("#### ⚡ Batch Operations")
                             with gr.Row():
-                                seed_btn = gr.Button("Seed Demo Data", variant="secondary")
-                                batch_btn = gr.Button("Re-index Folder", variant="secondary")
-                                clear_btn = gr.Button("Purge All Indexes", variant="stop")
+                                seed_btn  = gr.Button("Seed Demo Data",     variant="secondary")
+                                batch_btn = gr.Button("Re-index Folder",    variant="secondary")
+                                clear_btn = gr.Button("Purge All Indexes",  variant="stop")
                             action_log = gr.Markdown()
 
-                    # ── Tab 3: Workflow ────────────────────────────────────────
+                    # ── Tab 3: How It Works ───────────────────────────────────
                     with gr.Tab("🧠 How It Works"):
                         gr.Markdown("""
-                        ### Direct Multimodal Embedding
-                        ROCKIT doesn't use captioning models. It uses **Vision-Language Models (VLM)** to encode visual features 
-                        directly into the same vector space as text. This preserves subtle details that text captions often lose.
-                        """)
-                        
+### Direct Multimodal Embedding
+ROCKIT doesn't use captioning models. It uses **Vision-Language Models (VLM)** to encode
+visual features directly into the same vector space as text. This preserves subtle details
+that text captions often lose.
+""")
                         with gr.Row():
                             with gr.Column():
                                 gr.Markdown("#### 1. System Architecture")
@@ -346,9 +427,9 @@ def build_ui():
                                 gr.Markdown("#### 2. Query Flow")
                                 if os.path.exists(flow_path):
                                     gr.Image(flow_path, show_label=False)
-                        
+
                         gr.Markdown("---")
-                        
+
                         with gr.Row():
                             with gr.Column():
                                 gr.Markdown("#### 3. GPU Acceleration Tiers")
@@ -356,43 +437,81 @@ def build_ui():
                                     gr.Image(gpu_path, show_label=False)
                             with gr.Column():
                                 gr.Markdown("""
-                                #### Hot/Cold Memory Management
-                                To support dozens of projects on a single GPU, ROCKIT implements an **NVMe-to-VRAM Async Swap**.
-                                
-                                - **Cold Store (NVMe):** Indexes are serialized as `.cagra` files.
-                                - **Hot Cache (VRAM):** Active projects are copied into VRAM using pinned-memory DMA.
-                                - **LRU Eviction:** Least recently used indexes are purged from VRAM to make room for new ones.
-                                """)
-
-        # Event Bindings
-        project_select.change(fn=get_system_info, inputs=[project_select], outputs=[system_info])
-        refresh_btn.click(fn=lambda: gr.update(choices=get_projects_list()), outputs=[project_select])
-        
+#### Hot/Cold Memory Management
+To support dozens of projects on a single GPU, ROCKIT implements an **NVMe-to-VRAM Async Swap**.
+
+- **Cold Store (NVMe):** Indexes are serialized as `.cagra` files.
+- **Hot Cache (VRAM):** Active projects are copied into VRAM using pinned-memory DMA.
+- **LRU Eviction:** Least recently used indexes are purged from VRAM to make room for new ones.
+""")
+
+        # ── Event Bindings ────────────────────────────────────────────────────
+
+        # Sidebar controls
+        project_select.change(
+            fn=get_system_info,
+            inputs=[project_select],
+            outputs=[system_info],
+        )
+        refresh_btn.click(
+            fn=refresh_projects,
+            inputs=[],
+            outputs=[project_select],
+        )
         create_btn.click(
             fn=handle_create_project,
             inputs=[new_project_name],
             outputs=[create_status, project_select],
         )
 
+        # Search
+        _search_inputs  = [query_input, search_mode, top_k, project_select]
+        _search_outputs = [search_summary, result_gallery, store_info]
+
         search_btn.click(
-            fn=handle_search, 
-            inputs=[query_input, search_mode, top_k, project_select], 
-            outputs=[search_summary, result_gallery, store_info]
+            fn=handle_search,
+            inputs=_search_inputs,
+            outputs=_search_outputs,
         )
         query_input.submit(
-            fn=handle_search, 
-            inputs=[query_input, search_mode, top_k, project_select], 
-            outputs=[search_summary, result_gallery, store_info]
+            fn=handle_search,
+            inputs=_search_inputs,
+            outputs=_search_outputs,
+        )
+
+        # Ingest
+        img_btn.click(
+            fn=handle_image_upload,
+            inputs=[img_upload, project_select],
+            outputs=[img_log, system_info],
+        )
+        vid_btn.click(
+            fn=handle_video_upload,
+            inputs=[vid_upload, project_select],
+            outputs=[vid_log, system_info],
         )
 
-        img_btn.click(fn=handle_image_upload, inputs=[img_upload, project_select], outputs=[img_log, system_info])
-        vid_btn.click(fn=handle_video_upload, inputs=[vid_upload, project_select], outputs=[vid_log, system_info])
-        seed_btn.click(fn=handle_seed, inputs=[project_select], outputs=[action_log, system_info])
-        batch_btn.click(fn=handle_batch_ingest, inputs=[project_select], outputs=[action_log, system_info])
-        clear_btn.click(fn=handle_clear, inputs=[project_select], outputs=[action_log, system_info])
+        # Batch operations
+        seed_btn.click(
+            fn=handle_seed,
+            inputs=[project_select],
+            outputs=[action_log, system_info],
+        )
+        batch_btn.click(
+            fn=handle_batch_ingest,
+            inputs=[project_select],
+            outputs=[action_log, system_info],
+        )
+        clear_btn.click(
+            fn=handle_clear,
+            inputs=[project_select],
+            outputs=[action_log, system_info],
+        )
 
     return app
 
+# ── Entry point ───────────────────────────────────────────────────────────────
+
 if __name__ == "__main__":
     if seed_data.is_needed():
         logger.info("Auto-seeding default project from HF Dataset...")
@@ -402,4 +521,4 @@ if __name__ == "__main__":
             logger.error(f"Auto-seeding failed: {e}")
 
     app = build_ui()
-    app.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    app.launch(server_name="0.0.0.0", server_port=7860, share=False)