Create app.py

app.py

@@ -0,0 +1,168 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import tempfile
+import os
+
+
+def calculate_nps(scores):
+    scores = np.array(scores)
+    total = len(scores)
+    if total == 0:
+        return 0
+    promoters = np.sum((scores == 4) | (scores == 5))
+    detractors = np.sum((scores == 1) | (scores == 2))
+    return (promoters - detractors) / total * 100
+
+
+def adjust_nps(file, last_data_row, desired_nps_row, progress=gr.Progress(track_tqdm=False)):
+    if file is None:
+        raise gr.Error("Please upload an Excel file.")
+
+    if last_data_row is None or desired_nps_row is None:
+        raise gr.Error("Please enter both row numbers.")
+
+    try:
+        progress(0, desc="Reading file...")
+        df = pd.read_excel(file.name, sheet_name="Data", header=None)
+    except Exception as e:
+        raise gr.Error(f"Could not read sheet named 'Data'. Error: {e}")
+
+    # Convert user-friendly row numbers (e.g., 1503) to 0-based indices
+    individual_score_start = 3          # as in your original script
+    individual_score_end = int(last_data_row) - 1
+    desired_nps_row_idx = int(desired_nps_row) - 1
+
+    if individual_score_end <= individual_score_start:
+        raise gr.Error("LAST data record row must be greater than 4 (since data starts at row 4).")
+
+    last_column_index = df.shape[1] - 1
+    total_columns = last_column_index + 1
+
+    for col in range(0, last_column_index + 1):
+        progress(col / total_columns, desc=f"Processing column {col + 1}/{total_columns}")
+
+        # Get desired NPS for this column
+        try:
+            desired_nps = float(df.at[desired_nps_row_idx, col])
+        except (ValueError, TypeError):
+            continue  # skip if not a valid desired NPS
+        except KeyError:
+            continue
+
+        original_scores = df.loc[individual_score_start:individual_score_end, col].copy()
+        scores = pd.to_numeric(original_scores, errors="coerce").dropna()
+
+        if len(scores) < 10:
+            # Not enough data, skip column
+            continue
+
+        neutral_cap = np.random.uniform(3, 12)
+        current_nps = calculate_nps(scores)
+        changes = 0
+        max_changes = 400
+
+        while abs(current_nps - desired_nps) > 0.1 and changes < max_changes:
+            neutral_percent = (scores == 3).sum() / len(scores) * 100
+
+            if current_nps > desired_nps:
+                # Reduce NPS
+                candidates = scores[(scores == 5) | (scores == 4)].index
+                if len(candidates):
+                    idx = np.random.choice(candidates)
+                    scores.loc[idx] = 4 if scores.loc[idx] == 5 else 3
+                    changes += 1
+                elif neutral_percent > 0:
+                    candidates = scores[scores == 3].index
+                    if len(candidates):
+                        idx = np.random.choice(candidates)
+                        scores.loc[idx] = 2
+                        changes += 1
+                    else:
+                        break
+                else:
+                    break
+            else:
+                # Increase NPS
+                if neutral_percent < neutral_cap:
+                    candidates = scores[scores == 2].index
+                    if len(candidates):
+                        idx = np.random.choice(candidates)
+                        scores.loc[idx] = 3
+                        changes += 1
+                    else:
+                        candidates = scores[scores == 3].index
+                        if len(candidates):
+                            idx = np.random.choice(candidates)
+                            scores.loc[idx] = 4
+                            changes += 1
+                        else:
+                            break
+                else:
+                    candidates = scores[scores == 3].index
+                    if len(candidates):
+                        idx = np.random.choice(candidates)
+                        scores.loc[idx] = 4
+                        changes += 1
+                    else:
+                        break
+
+            current_nps = calculate_nps(scores)
+
+        # Write back adjusted scores
+        df.loc[individual_score_start:individual_score_end, col] = scores
+
+    progress(1, desc="Writing adjusted file...")
+
+    # Save output to a temp file and return path
+    temp_dir = tempfile.mkdtemp()
+    output_path = os.path.join(temp_dir, "NeuroSinQ_NPS_Adjusted.xlsx")
+    df.to_excel(output_path, index=False, header=False)
+
+    return output_path
+
+
+with gr.Blocks(title="NeuroSinQ NPS Adjuster") as demo:
+    gr.Markdown(
+        """
+        # 🧠 NeuroSinQ NPS Adjuster
+
+        1. Upload an Excel file with **one worksheet** named **`Data`**.  
+        2. Enter the **row number of LAST data record**.  
+        3. Enter the **row number of Desired NPS**.  
+        4. Click **Make Adjustment** to download the adjusted file.
+        """
+    )
+
+    with gr.Row():
+        file_input = gr.File(
+            label="Upload Excel File (.xlsx)",
+            file_types=[".xlsx"],
+        )
+
+    with gr.Row():
+        last_row = gr.Number(
+            label="Row number of LAST data record (e.g., 1503)",
+            precision=0
+        )
+        desired_row = gr.Number(
+            label="Row number for Desired NPS (e.g., 1523)",
+            precision=0
+        )
+
+    run_btn = gr.Button("⚙️ Make Adjustment")
+
+    output_file = gr.File(
+        label="Download Adjusted File",
+        interactive=False
+    )
+
+    run_btn.click(
+        fn=adjust_nps,
+        inputs=[file_input, last_row, desired_row],
+        outputs=output_file
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()