Update dvnc_ai_v2_hf/app.py

dvnc_ai_v2_hf/app.py

@@ -1566,37 +1566,112 @@ with gr.Blocks(css=CSS, head=HEAD, theme=gr.themes.Base(), fill_height=True) as
         </div>
         """
     )
+    
+        with gr.Tabs():
+        with gr.Tab("Discovery Engine"):
+            model_html = gr.HTML(build_models_html("DVNC Sovereign"))
+            with gr.Row():
+                with gr.Column(scale=2):
+                    model = gr.Dropdown(
+                        choices=[m["name"] for m in MODELS],
+                        value="DVNC Sovereign",
+                        label="Model tier",
+                    )
+                    query = gr.Textbox(
+                        label="Discovery query",
+                        elem_classes=["querybox"],
+                        placeholder="Enter a scientific question, anomaly, or breakthrough direction…",
+                        lines=4,
+                    )
+                    with gr.Row():
+                        run_btn = gr.Button("Run discovery", variant="primary")
+                        example_btn = gr.Button("Load example", variant="secondary")
+                    chat = gr.HTML(
+                        """
+                        <div class="panel chat-panel">
+                          <div class="chat-thread">
+                            <div class="bubble bubble-ai">
+                              <span class="role">DVNC</span>
+                              <p>Enter a query to activate the 7-agent discovery stack and illuminate the chosen path through the 3D connectome.</p>
+                            </div>
+                          </div>
+                        </div>
+                        """
+                    )
+                with gr.Column(scale=3):
+                    connectome = gr.HTML(build_connectome_html(DEFAULT_PATH))
+
+                    # Legacy placeholder retained for rollback.
+                    # cards = gr.HTML('<div class="panel" style="padding:20px"><div class="candidate-grid"></div></div>')
+                    cards = gr.HTML("")
 
-    with gr.Tab("Discovery Engine"):
-    model_html = gr.HTML(build_models_html("DVNC Sovereign"))
-    with gr.Row():
-        with gr.Column(scale=2):
-            model = gr.Dropdown(
-                choices=[m["name"] for m in MODELS],
-                value="DVNC Sovereign",
-                label="Model tier",
+            output = gr.Markdown("# Discovery Output\n\nAwaiting query.")
+            timeline = gr.HTML(get_initial_discovery_timeline_html())
+
+            route_swap_payload = gr.Textbox(
+                value="",
+                visible=False,
+                elem_id="route_swap_payload",
             )
-            query = gr.Textbox(
-                label="Discovery query",
-                elem_classes=["querybox"],
-                placeholder="Enter a scientific question, anomaly, or breakthrough direction…",
-                lines=4,
+            route_swap_apply = gr.Button(
+                "Apply route swap",
+                visible=False,
+                elem_id="route_swap_apply",
             )
+
+        with gr.Tab("Self-Learning Graph"):
             with gr.Row():
-                run_btn = gr.Button("Run discovery", variant="primary")
-                example_btn = gr.Button("Load example", variant="secondary")
-            chat = gr.HTML(
-                """
-                <div class="panel chat-panel">
-                  <div class="chat-thread">
-                    <div class="bubble bubble-ai">
-                      <span class="role">DVNC</span>
-                      <p>Enter a query to activate the 7-agent discovery stack and illuminate the chosen path through the 3D connectome.</p>
-                    </div>
-                  </div>
-                </div>
-                """
-            )
+                with gr.Column(scale=2):
+                    paper_query = gr.Textbox(
+                        label="Research topic / title / DOI / link",
+                        elem_classes=["querybox"],
+                        placeholder="e.g. self-assembling conductive biomaterials for cardiac repair",
+                        lines=3,
+                    )
+                    search_mode = gr.Dropdown(
+                        choices=SEARCH_MODES,
+                        value="topic",
+                        label="Search mode"
+                    )
+                    source_selector = gr.CheckboxGroup(
+                        choices=SOURCE_OPTIONS,
+                        value=DEFAULT_SOURCES,
+                        label="Sources"
+                    )
+                    pdf_upload = gr.File(label="Upload PDF papers", file_types=[".pdf"], file_count="single")
+
+                    with gr.Row():
+                        learn_btn = gr.Button("Discover papers", variant="primary")
+                        load_topic_btn = gr.Button("Load example topic", variant="secondary")
+
+                    upload_status = gr.Markdown("No PDF uploaded yet.")
+                    discovery_status = gr.Markdown("### No discovery results yet.")
+                    journal_panel = gr.HTML(build_journal_html("biomaterials cardiac repair"))
+
+                    gr.HTML('<div class="panel selection-panel"><h3 style="margin:0 0 12px;">Select papers to ingest</h3></div>')
+                    selection_box = gr.CheckboxGroup(
+                        choices=[],
+                        value=[],
+                        label="Candidate papers"
+                    )
+
+                    parser_order = gr.CheckboxGroup(
+                        choices=["grobid", "docling", "pymupdf"],
+                        value=["grobid", "docling", "pymupdf"],
+                        label="Parser routing order"
+                    )
+
+                    with gr.Row():
+                        parse_btn = gr.Button("Parse uploaded PDF", variant="secondary")
+                        ingest_btn = gr.Button("Ingest selected into graph", variant="primary")
+
+                with gr.Column(scale=3):
+                    learning_graph = gr.HTML(build_learning_graph_html([], []))
+                    papers_panel = gr.HTML('<div class="panel papers-panel" style="padding:18px"><p>Search by topic, title, DOI, or link, then select papers before graph ingestion.</p></div>')
+                    parse_summary = gr.Markdown("### PDF parse status\n\nAwaiting upload.")
+                    parse_panel = gr.HTML('<div class="panel" style="padding:18px"><p>No parsed document yet.</p></div>')
+                    ingest_summary = gr.Markdown("### Graph ingest status\n\nAwaiting paper selection.")
+                    ingest_payload = gr.JSON(label="Graph ingest payload", value={"status": "empty", "nodes": [], "edges": []})
         with gr.Column(scale=3):
             connectome = gr.HTML(build_connectome_html(DEFAULT_PATH))
 
@@ -1717,15 +1792,15 @@ with gr.Blocks(css=CSS, head=HEAD, theme=gr.themes.Base(), fill_height=True) as
     example_btn.click(fn=load_example, outputs=query)
 
     run_btn.click(
-    fn=run_discovery,
-    inputs=[query, model],
-    outputs=[chat, connectome, timeline, cards, output, model_html, route_state],
-     )
+        fn=run_discovery,
+        inputs=[query, model],
+        outputs=[chat, connectome, timeline, cards, output, model_html, route_state],
+    )
 
     route_swap_apply.click(
-    fn=apply_route_swap,
-    inputs=[query, model, route_swap_payload, route_state],
-    outputs=[chat, connectome, timeline, output, route_state],
+        fn=apply_route_swap,
+        inputs=[query, model, route_swap_payload, route_state],
+        outputs=[chat, connectome, timeline, output, route_state],
     )
 
     load_topic_btn.click(fn=load_paper_topic, outputs=paper_query)