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470412d 030fa79 470412d 030fa79 470412d 030fa79 470412d 030fa79 470412d 030fa79 470412d 030fa79 470412d 030fa79 470412d 030fa79 470412d 030fa79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | # app.py (updated)
import gradio as gr
import pandas as pd
import numpy as np
import os
import tempfile
from finance_utils import preprocess_transactions, summarize_transactions, predict_expenses, category_summary
DATA_DIR = "data"
def load_bundled_datasets():
files = [f for f in os.listdir(DATA_DIR) if f.lower().endswith((".csv",".xlsx",".xls"))]
datasets = {}
for f in files:
path = os.path.join(DATA_DIR, f)
try:
df = pd.read_csv(path)
except Exception:
try:
df = pd.read_excel(path)
except Exception:
continue
datasets[f] = df
return datasets
def analyze_uploaded(files, use_bundled=True):
combined = []
if use_bundled:
bundled = load_bundled_datasets()
for name, df in bundled.items():
combined.append(df)
if files:
for f in files:
# gr.File gives a file-like with .name pointing to a temp file path on the server
try:
df = pd.read_csv(f.name)
except Exception:
try:
df = pd.read_excel(f.name)
except Exception:
continue
combined.append(df)
if not combined:
return "No data found. Upload CSV(s) or include datasets in the data/ folder of the Space repository.", None
df = pd.concat(combined, ignore_index=True, sort=False)
df_clean = preprocess_transactions(df)
summary = summarize_transactions(df_clean)
preds = predict_expenses(df_clean)
cat_summary = category_summary(df_clean)
# save processed CSV to temporary file for download
tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
df_clean.to_csv(tmp.name, index=False)
tmp.close()
# return summary text and path to file (gr.File will serve that path)
return summary, tmp.name
title = "BudgetBuddy — Autonomous Personal Finance Agent"
description = """
Upload your bank transaction CSVs (or include them in the Space `data/` folder). BudgetBuddy will:
- Parse and standardize transactions (date, amount, description)
- Auto-categorize spending (rules + ML fallback)
- Produce monthly summaries and category breakdowns
- Predict next month's expenses per category (simple model)
- Export a cleaned CSV with categories for further analysis
"""
with gr.Blocks() as demo:
gr.Markdown(f"# {title}")
gr.Markdown(description)
with gr.Row():
upload = gr.File(file_count="multiple", label="Upload bank CSV(s) (optional)")
use_bundled = gr.Checkbox(value=True, label="Also load bundled datasets from data/ folder")
analyze_btn = gr.Button("Analyze")
output_text = gr.Textbox(lines=12, label="Summary")
output_file = gr.File(label="Download processed CSV") # gr.File used for download
analyze_btn.click(fn=analyze_uploaded, inputs=[upload, use_bundled], outputs=[output_text, output_file])
if __name__ == "__main__":
demo.launch()
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