Upload 2 files

app.py

@@ -13,7 +13,12 @@ from quread.llm_explain_openai import explain_with_gpt4o
 from quread.circuit_diagram import draw_circuit_svg
 from quread.cost_guard import allow_request
 from quread.export_pdf import md_to_pdf
-from quread.heatmap import make_metric_heatmap, HeatmapConfig
+from quread.heatmap import (
+    make_metric_heatmap,
+    make_metric_heatmap_plotly,
+    plotly_available,
+    HeatmapConfig,
+)
 from quread.metrics import (
     compute_metrics_from_csv,
     to_metrics_csv,
@@ -467,6 +472,11 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
                                     value="activity_count",
                                     label="Metric",
                                 )
+                                heat_render_mode = gr.Dropdown(
+                                    choices=["interactive", "static"],
+                                    value="interactive",
+                                    label="Render mode",
+                                )
                                 chip_rows = gr.Slider(2, 64, value=8, step=1, label="Chip rows")
                                 chip_cols = gr.Slider(2, 64, value=8, step=1, label="Chip cols")
                                 metric_threshold = gr.Slider(
@@ -659,6 +669,7 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
         rows,
         cols,
         metric,
+        render_mode,
         calibration_text,
         activity_w,
         gate_error_w,
@@ -670,7 +681,7 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
         metric_thr,
         focus_qubit,
         top_k,
-    ):
+        ):
         csv_text = to_csv(qc.history)  # must exist from Task 2A
         cfg = HeatmapConfig(rows=int(rows), cols=int(cols))
         weights, thresholds = _metric_controls_to_models(
@@ -682,17 +693,49 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
             warning_thr,
             critical_thr,
         )
-        fig = make_metric_heatmap(
-            csv_text=csv_text,
-            n_qubits=int(n_qubits),
-            metric=str(metric),
-            cfg=cfg,
-            calibration_json=str(calibration_text or ""),
-            state_vector=np.asarray(qc.state, dtype=complex),
-            weights=weights,
-            thresholds=thresholds,
-            highlight_threshold=(None if float(metric_thr) <= 0 else float(metric_thr)),
-        )
+        threshold_value = None if float(metric_thr) <= 0 else float(metric_thr)
+        render_choice = str(render_mode or "interactive").strip().lower()
+        notes = []
+
+        if render_choice == "interactive":
+            if plotly_available():
+                fig = make_metric_heatmap_plotly(
+                    csv_text=csv_text,
+                    n_qubits=int(n_qubits),
+                    metric=str(metric),
+                    cfg=cfg,
+                    calibration_json=str(calibration_text or ""),
+                    state_vector=np.asarray(qc.state, dtype=complex),
+                    weights=weights,
+                    thresholds=thresholds,
+                    highlight_threshold=threshold_value,
+                )
+            else:
+                fig = make_metric_heatmap(
+                    csv_text=csv_text,
+                    n_qubits=int(n_qubits),
+                    metric=str(metric),
+                    cfg=cfg,
+                    calibration_json=str(calibration_text or ""),
+                    state_vector=np.asarray(qc.state, dtype=complex),
+                    weights=weights,
+                    thresholds=thresholds,
+                    highlight_threshold=threshold_value,
+                )
+                notes.append("Plotly unavailable in this runtime; using static heatmap.")
+        else:
+            fig = make_metric_heatmap(
+                csv_text=csv_text,
+                n_qubits=int(n_qubits),
+                metric=str(metric),
+                cfg=cfg,
+                calibration_json=str(calibration_text or ""),
+                state_vector=np.asarray(qc.state, dtype=complex),
+                weights=weights,
+                thresholds=thresholds,
+                highlight_threshold=threshold_value,
+            )
+
         metrics, meta = compute_metrics_from_csv(
             csv_text,
             int(n_qubits),
@@ -702,7 +745,7 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
             thresholds=thresholds,
         )
         hotspot_rows = _hotspot_rows(metrics, int(n_qubits), int(top_k))
-        note = []
+        note = notes
         skipped = int(meta.get("skipped_rows", 0))
         if skipped:
             note.append(f"Skipped malformed CSV rows: {skipped}")
@@ -757,6 +800,7 @@ with gr.Blocks(theme=theme, css=CSS, title="Quread.ai — State Vector Studio")
             chip_rows,
             chip_cols,
             heat_metric,
+            heat_render_mode,
             calibration_json,
             w_activity,
             w_gate,

requirements.txt

@@ -1,6 +1,7 @@
 gradio>=4.0.0
 numpy
 matplotlib
+plotly>=5.0.0
 requests
 huggingface_hub>=0.30.0
 transformers==4.45.2
@@ -9,4 +10,4 @@ optimum[onnxruntime]>=1.20.0
 onnxruntime>=1.17.0
 openai>=1.0.0
 markdown
-reportlab
+reportlab