# 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()