Update app.py

app.py

@@ -1,82 +1,98 @@
 import pandas as pd
-import matplotlib
-matplotlib.use("Agg")
-import matplotlib.pyplot as plt
 import gradio as gr
-import os
-import tempfile
-
-def plot_top_100_mak(excel_file):
-    # Baca file Excel dari path
-    df = pd.read_excel(excel_file)
-
-    # Tangani nilai kosong
-    df["MAK"] = df["MAK"].fillna("Unknown")
-    df["Nominal"] = pd.to_numeric(df["Nominal"], errors="coerce").fillna(0)
-
-    # Ambil 100 MAK dengan nominal terbesar
-    top_100 = (
-        df.groupby("MAK", as_index=False)["Nominal"]
-        .sum()
-        .sort_values("Nominal", ascending=False)
-        .head(100)
-    )
-
-    # Konversi ke juta
-    top_100["Nominal"] = top_100["Nominal"] / 1_000_000
-
-    # Plot
-    plt.figure(figsize=(16, 9))
-    bars = plt.bar(top_100["MAK"], top_100["Nominal"])
-    plt.xlabel("MAK")
-    plt.ylabel("Nominal (Juta)")
-    plt.title("Top 100 MAK dengan Nominal Terbesar")
-    plt.xticks(rotation=90)
-    plt.tight_layout()
-
-    # Label nilai
-    for bar in bars:
-        height = bar.get_height()
-        plt.text(
-            bar.get_x() + bar.get_width() / 2,
-            height,
-            f"{height:,.2f}",
-            ha="center",
-            va="bottom",
-            fontsize=7,
-        )
-
-    # Simpan ke file sementara
-    tmp_dir = tempfile.mkdtemp()
-    output_path = os.path.join(tmp_dir, "top_100_mak.png")
-    plt.savefig(output_path, dpi=150)
-    plt.close()
-
-    return "Bar chart berhasil dibuat.", output_path
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """
-        ## 📊 Top 100 MAK dengan Nominal Terbesar
-        Unggah file Excel yang memiliki kolom **MAK** dan **Nominal**
-        """
-    )
-
-    file_input = gr.File(
-        label="Unggah File Excel",
-        file_types=[".xlsx", ".xls"],
-    )
-
-    result_text = gr.Textbox(label="Status")
-    result_image = gr.Image(label="Bar Chart")
-
-    submit_btn = gr.Button("Proses")
-
-    submit_btn.click(
-        fn=plot_top_100_mak,
-        inputs=file_input,
-        outputs=[result_text, result_image],
-    )
+
+def process_data(file):
+    try:
+        # file adalah path di Hugging Face Spaces
+        df = pd.read_excel(file)
+
+        output_data = []
+        no = 1
+
+        char1 = char2 = char3 = char4 = ""
+        previous_char3 = ""
+        nominal = tipe = 0
+        anggaran1 = anggaran2 = anggaran3 = 0
+        total_nominal = 0
+
+        column_names = df.columns.tolist()
+
+        for _, row in df.iterrows():
+            kode = str(row[column_names[0]]).strip()
+            anggaran = row[column_names[3]]
+
+            if kode and kode[0] in ["3", "6"]:
+                char1 = kode
+                anggaran3 = anggaran
+
+            elif kode.startswith("0"):
+                char2 = kode
+
+            elif kode != "nan" and kode.isalpha():
+                char3 = kode
+                previous_char3 = char3
+                anggaran1 = anggaran
+
+            elif kode.startswith("5"):
+                char4 = kode
+                anggaran2 = anggaran
+
+                if pd.isna(row[column_names[9]]):
+                    nominal = row[column_names[10]]
+                    tipe = row[column_names[13]]
+                else:
+                    nominal = row[column_names[9]]
+                    tipe = row[column_names[11]]
+
+                if char3 == "":
+                    char3 = previous_char3
+
+                mak = f"{char1}.{char2}.{char3}.{char4}"
+
+                output_data.append({
+                    "No": no,
+                    "MAK": mak,
+                    "Anggaran3": anggaran3,
+                    "Anggaran1": anggaran1,
+                    "Anggaran2": anggaran2,
+                    "Nominal": nominal,
+                    "Tipe": tipe
+                })
+
+                no += 1
+                char3 = ""
+
+                if pd.notna(nominal):
+                    total_nominal += float(nominal)
+
+        output_data.append({
+            "No": "",
+            "MAK": "",
+            "Anggaran3": "",
+            "Anggaran1": "",
+            "Anggaran2": "",
+            "Nominal": f"Total Nominal: {total_nominal:.0f}",
+            "Tipe": ""
+        })
+
+        output_df = pd.DataFrame(output_data)
+
+        output_file = "/tmp/processed_data.xlsx"
+        output_df.to_excel(output_file, index=False)
+
+        return output_file
+
+    except Exception as e:
+        return f"Error: {e}"
+
+with gr.Blocks(title="Konvert Mata Anggaran Sakti") as demo:
+    gr.Markdown("### Konvert Mata Anggaran Sakti")
+    gr.Markdown("Unggah file Excel, sistem akan mengonversi dan menghasilkan file Excel baru.")
+
+    input_file = gr.File(label="Upload Excel File", file_types=[".xlsx"])
+    output_file = gr.File(label="Download Hasil")
+
+    btn = gr.Button("Proses")
+    btn.click(process_data, inputs=input_file, outputs=output_file)
 
 demo.launch()