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fengshui_app.py

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+import gradio as gr
+import pandas as pd
+
+def clean_text(text):
+    if not isinstance(text, str):
+        return text
+    text = text.replace('\n', ' ').replace('\r', ' ')
+    return ' '.join(text.split())
+
+def generate_reports(file, inputs):
+    excel_data = pd.ExcelFile(file.name)
+    clean_sheet_names = [name.strip() for name in excel_data.sheet_names]
+
+    output_rows = []
+    output_rows_perm = []
+
+    for item in inputs:
+        dir_name = item['Direction']
+        bagua_area = item['Bagua']
+        pm = int(item['PM'])
+        pm_star = int(item['PM*'])
+        an = int(item['AN'])
+
+        # Annual
+        sheet_name = clean_sheet_names[pm - 1]
+        df = excel_data.parse(sheet_name)
+        match = df[(df['PM*'] == pm_star) & (df['AN'] == an)]
+        if match.empty:
+            sheet_name = clean_sheet_names[pm_star - 1]
+            df = excel_data.parse(sheet_name)
+            match = df[(df['PM*'] == pm) & (df['AN'] == an)]
+        if match.empty:
+            result = {
+                "Positive Energy": "No data found",
+                "Negative Energy": "No data found",
+                "REMEDY": "No data found",
+                "ADDITIONAL": "No data found",
+                "INTERPRETATIONS": "No data found"
+            }
+        else:
+            row = match.iloc[0]
+            result = {
+                "Positive Energy": clean_text(row['POSITIVE ENERGY']),
+                "Negative Energy": clean_text(row['NEGATIVE ENERGY']),
+                "REMEDY": clean_text(row['REMEDY']),
+                "ADDITIONAL": clean_text(row['ADDITIONAL']),
+                "INTERPRETATIONS": clean_text(row['INTERPRETATIONS'])
+            }
+        output_rows.append({
+            "Direction": dir_name,
+            "Bagua Area": bagua_area,
+            "PM": pm,
+            "PM*": pm_star,
+            "AN": an,
+            **result
+        })
+
+        # Permanent
+        sheet_name = clean_sheet_names[pm - 1]
+        df = excel_data.parse(sheet_name)
+        match_perm = df[(df['PM*'] == pm_star) & (df['AN'].isna())]
+        if match_perm.empty:
+            sheet_name = clean_sheet_names[pm_star - 1]
+            df = excel_data.parse(sheet_name)
+            match_perm = df[(df['PM*'] == pm) & (df['AN'].isna())]
+        if match_perm.empty:
+            result_perm = {
+                "Positive Energy": "No data found",
+                "Negative Energy": "No data found"
+            }
+        else:
+            row = match_perm.iloc[0]
+            result_perm = {
+                "Positive Energy": clean_text(row['POSITIVE ENERGY']),
+                "Negative Energy": clean_text(row['NEGATIVE ENERGY'])
+            }
+        output_rows_perm.append({
+            "Direction": dir_name,
+            "Bagua Area": bagua_area,
+            "PM": pm,
+            "PM*": pm_star,
+            **result_perm
+        })
+
+    df_annual = pd.DataFrame(output_rows)
+    annual_file = "Annual_Report_Populated.csv"
+    df_annual.to_csv(annual_file, index=False)
+
+    df_perm = pd.DataFrame(output_rows_perm)
+    perm_file = "Permanent_Report_Populated.csv"
+    df_perm.to_csv(perm_file, index=False)
+
+    return annual_file, perm_file
+
+directions = [
+    {"Direction": "West", "Bagua": "Children & Creativity"},
+    {"Direction": "North West", "Bagua": "Helpful People"},
+    {"Direction": "North", "Bagua": "Career & Life’s Journey"},
+    {"Direction": "North East", "Bagua": "Knowledge & Self-Cultivation"},
+    {"Direction": "East", "Bagua": "Family & Community"},
+    {"Direction": "South East", "Bagua": "Wealth & Prosperity"},
+    {"Direction": "South", "Bagua": "Fame & Recognition"},
+    {"Direction": "South West", "Bagua": "Love & Marriage"},
+    {"Direction": "Centre", "Bagua": "Health"}
+]
+
+def collect_input(*args):
+    result = []
+    for i, d in enumerate(directions):
+        result.append({
+            "Direction": d["Direction"],
+            "Bagua": d["Bagua"],
+            "PM": args[i*3],
+            "PM*": args[i*3+1],
+            "AN": args[i*3+2]
+        })
+    return result
+
+with gr.Blocks() as demo:
+    file = gr.File(label="Upload your Excel file")
+    number_inputs = []
+    for d in directions:
+        pm = gr.Number(label=f"{d['Direction']} PM")
+        pm_star = gr.Number(label=f"{d['Direction']} PM*")
+        an = gr.Number(label=f"{d['Direction']} AN")
+        number_inputs.extend([pm, pm_star, an])
+
+    generate_button = gr.Button("Generate Reports")
+    annual_out = gr.File(label="Download Annual Report")
+    perm_out = gr.File(label="Download Permanent Report")
+
+    def wrapper(file, *args):
+        inputs = collect_input(*args)
+        return generate_reports(file, inputs)
+
+    generate_button.click(wrapper, inputs=[file, *number_inputs], outputs=[annual_out, perm_out])
+
+demo.launch(share=True)

requirements.txt

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+gradio
+pandas
+openpyxl