feat: show input data and encoded parameters previews

app.py

@@ -81,6 +81,9 @@ ENCODING_DESC = {
 # Core run function
 # ---------------------------------------------------------------------------
 
+_EMPTY_DF = pd.DataFrame()
+
+
 def run_quprep(
     csv_file,
     sample_name: str,
@@ -88,19 +91,21 @@ def run_quprep(
     framework: str,
     n_samples: int,
     n_qubits: int,
-) -> tuple[str, str, str]:
+) -> tuple[pd.DataFrame, pd.DataFrame, str, str, str]:
     """
-    Returns (circuit_output, cost_info, status_message).
+    Returns (input_preview, encoded_preview, circuit_output, cost_info, status_message).
     """
     try:
         import quprep as qd
     except ImportError:
-        return "", "", "❌ quprep is not installed in this Space."
+        return _EMPTY_DF, _EMPTY_DF, "", "", "❌ quprep is not installed in this Space."
 
     # --- load data ---
     try:
         if csv_file is not None:
-            df = pd.read_csv(csv_file.name)
+            # Gradio 6.x returns a filepath string; older versions return a file object
+            csv_path = csv_file if isinstance(csv_file, str) else csv_file.name
+            df = pd.read_csv(csv_path)
         elif sample_name and sample_name in SAMPLES:
             df = pd.read_csv(io.StringIO(SAMPLES[sample_name]()))
         else:
@@ -123,7 +128,7 @@ def run_quprep(
             tmp_path = tmp.name
 
     except Exception as exc:
-        return "", "", f"❌ Data loading error: {exc}"
+        return _EMPTY_DF, _EMPTY_DF, "", "", f"❌ Data loading error: {exc}"
 
     # --- run pipeline ---
     try:
@@ -143,13 +148,13 @@ def run_quprep(
 
     except ImportError as exc:
         missing = str(exc)
-        return "", "", (
+        return _EMPTY_DF, _EMPTY_DF, "", "", (
             f"⚠️ Optional dependency not installed in this Space: {missing}\n"
             f"Try selecting **qasm** as the framework, or install the extra."
         )
     except Exception as exc:
         tb = traceback.format_exc()
-        return "", "", f"❌ Pipeline error:\n{tb}"
+        return _EMPTY_DF, _EMPTY_DF, "", "", f"❌ Pipeline error:\n{tb}"
     finally:
         try:
             os.unlink(tmp_path)
@@ -159,7 +164,26 @@ def run_quprep(
     # --- format output ---
     circuits = result.circuits or []
     if not circuits:
-        return "", "", "⚠️ No circuits produced."
+        return _EMPTY_DF, _EMPTY_DF, "", "", "⚠️ No circuits produced."
+
+    # input preview — first 5 rows of cleaned df
+    input_preview = df.head(5).round(4)
+
+    # encoded parameters preview — parameters array from each encoded result
+    encoded_list = result.encoded or []
+    if encoded_list:
+        try:
+            rows = []
+            for i, enc in enumerate(encoded_list[:5]):
+                params = enc.parameters
+                row = {f"q{j}": round(float(p), 4) for j, p in enumerate(params)}
+                row = {"sample": i, **row}
+                rows.append(row)
+            encoded_preview = pd.DataFrame(rows).set_index("sample")
+        except Exception:
+            encoded_preview = _EMPTY_DF
+    else:
+        encoded_preview = _EMPTY_DF
 
     # show first circuit as text
     first = circuits[0]
@@ -193,7 +217,7 @@ def run_quprep(
         f"→ {n_total} circuit(s). Showing sample 0."
     )
 
-    return circuit_text, cost_text, status
+    return input_preview, encoded_preview, circuit_text, cost_text, status
 
 
 # ---------------------------------------------------------------------------
@@ -208,7 +232,8 @@ def run_recommend(csv_file, sample_name: str, task: str, n_qubits: int) -> str:
 
     try:
         if csv_file is not None:
-            df = pd.read_csv(csv_file.name)
+            csv_path = csv_file if isinstance(csv_file, str) else csv_file.name
+            df = pd.read_csv(csv_path)
         elif sample_name and sample_name in SAMPLES:
             df = pd.read_csv(io.StringIO(SAMPLES[sample_name]()))
         else:
@@ -250,7 +275,8 @@ def run_compare(csv_file, sample_name: str, task: str, n_qubits: int) -> str:
 
     try:
         if csv_file is not None:
-            df = pd.read_csv(csv_file.name)
+            csv_path = csv_file if isinstance(csv_file, str) else csv_file.name
+            df = pd.read_csv(csv_path)
         elif sample_name and sample_name in SAMPLES:
             df = pd.read_csv(io.StringIO(SAMPLES[sample_name]()))
         else:
@@ -341,8 +367,11 @@ Framework-agnostic: export to Qiskit, PennyLane, Cirq, TKET, Braket, Q#, IQM, or
 
                 with gr.Column(scale=2):
                     status_out  = gr.Textbox(label="Status", lines=2, interactive=False)
-                    circuit_out = gr.Code(label="Circuit output (sample 0)", language="shell", lines=20)
+                    input_table = gr.Dataframe(label="Input data (first 5 rows)", interactive=False)
+                    circuit_out = gr.Code(label="Circuit output (sample 0)", language="python", lines=20)
                     cost_out    = gr.Textbox(label="Cost estimate", lines=8, interactive=False)
+                    with gr.Accordion("Encoded parameters (first 5 rows)", open=False):
+                        encoded_table = gr.Dataframe(interactive=False)
 
             enc_dd.change(
                 fn=lambda e: f"*{ENCODING_DESC.get(e, '')}*",
@@ -353,7 +382,7 @@ Framework-agnostic: export to Qiskit, PennyLane, Cirq, TKET, Braket, Q#, IQM, or
             convert_btn.click(
                 fn=run_quprep,
                 inputs=[csv_upload, sample_dd, enc_dd, fw_dd, n_samples_sl, n_qubits_sl],
-                outputs=[circuit_out, cost_out, status_out],
+                outputs=[input_table, encoded_table, circuit_out, cost_out, status_out],
             )
 
         # ── Tab 2: Recommend ────────────────────────────────────────────────