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import streamlit as st |
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import pandas as pd |
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from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering |
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class InterestCalculatorApp: |
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def __init__(self): |
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self.invoices_df = None |
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self.base_rates_df = None |
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self.late_payment_interest = st.sidebar.slider("Late Payment Interest Rate (%)", min_value=0.0, max_value=10.0, value=4.0) |
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self.compounding_method = st.sidebar.selectbox("Compounding Method", ["x% above Base Rate (daily)", |
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"x% above Base Rate (annually)", |
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"Quarterly compounding (25 Mar, 24 Jun, 29 Sep, 25 Dec)", |
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"Quarterly compounding (1 Mar, 1 Jun, 1 Sep, 1 Dec)"]) |
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self.tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq") |
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self.model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq") |
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def load_invoices(self, file_path): |
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try: |
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self.invoices_df = pd.read_excel(file_path, parse_dates=['Due Date']) |
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st.success("Invoices loaded successfully.") |
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except Exception as e: |
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st.error(f"Failed to load invoices: {e}") |
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def calculate_interest(self): |
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if self.invoices_df is not None and self.base_rates_df is not None: |
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st.write("Calculating interest...") |
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today = datetime.today() |
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interests = [] |
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for index, invoice in self.invoices_df.iterrows(): |
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due_date = invoice['Due Date'] |
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amount = invoice['Amount'] |
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base_rate = self.get_base_rate(due_date) |
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effective_rate = base_rate + self.late_payment_interest |
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if due_date > today: |
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interests.append(0) |
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continue |
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interest = self.calculate_compound_interest(due_date, amount, effective_rate, self.compounding_method, today) |
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interests.append(interest) |
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total_amount_owed = amount + interest |
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self.invoices_df.loc[index, 'Interest'] = interest |
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self.invoices_df.loc[index, 'Total Amount Owed'] = total_amount_owed |
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total_interest = sum(interests) |
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st.success(f"Total Interest Calculated: £{total_interest:.2f}") |
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st.write(self.invoices_df) |
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else: |
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st.error("Please load both invoices and base rates files.") |
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def get_base_rate(self, due_date): |
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self.base_rates_df['Date Changed'] = pd.to_datetime(self.base_rates_df['Date Changed']) |
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rate_rows = self.base_rates_df[self.base_rates_df['Date Changed'] <= due_date].sort_values(by='Date Changed', ascending=False) |
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return rate_rows.iloc[0]['Rate'] if not rate_rows.empty else 0 |
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def calculate_compound_interest(self, due_date, amount, effective_rate, method, today): |
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days = (today - due_date).days |
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if 'daily' in method: |
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daily_rate = (effective_rate / 100) / 365 |
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return amount * daily_rate * days |
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elif 'annually' in method: |
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annual_rate = effective_rate / 100 |
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return amount * annual_rate * (days / 365) |
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elif 'Quarterly compounding' in method: |
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return self.calculate_quarterly_interest(due_date, amount, effective_rate, method, today) |
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def calculate_quarterly_interest(self, due_date, amount, effective_rate, method, today): |
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quarterly_dates = { |
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"Quarterly compounding (25 Mar, 24 Jun, 29 Sep, 25 Dec)": [(3, 25), (6, 24), (9, 29), (12, 25)], |
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"Quarterly compounding (1 Mar, 1 Jun, 1 Sep, 1 Dec)": [(3, 1), (6, 1), (9, 1), (12, 1)] |
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}[method] |
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interest = 0 |
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compounded_amount = amount |
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for month, day in quarterly_dates: |
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compounding_date = datetime(today.year, month, day) |
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if compounding_date > today: |
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break |
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if compounding_date > due_date: |
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days_since_last_compounding = (today - compounding_date).days |
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period_rate = effective_rate / 4 |
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compounded_interest = compounded_amount * ((1 + period_rate) ** (days_since_last_compounding / 365.25) - 1) |
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compounded_amount += compounded_interest |
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interest += compounded_interest |
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due_date = compounding_date |
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return interest |
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def download_boe_rates(self): |
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try: |
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headers = { |
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'accept-language': 'en-US,en;q=0.9', |
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'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36' |
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} |
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url = 'https://www.bankofengland.co.uk/boeapps/database/Bank-Rate.asp' |
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response = requests.get(url, headers=headers) |
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if response.status_code == 200: |
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df = pd.read_html(response.text)[0] |
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df.to_csv('boe_rates.csv', index=False) |
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self.base_rates_df = df |
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self.base_rates_df['Date Changed'] = pd.to_datetime(self.base_rates_df['Date Changed'], format='%d %b %y') |
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st.success("Bank of England rates downloaded successfully.") |
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else: |
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st.error("Failed to retrieve data from the Bank of England website.") |
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except requests.RequestException as e: |
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st.error(f"Failed to download rates: {e}") |
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def ask_tapas(self, query, table): |
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inputs = self.tokenizer(table, query, return_tensors="pt", padding=True) |
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outputs = self.model(**inputs) |
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predicted_answer = self.tokenizer.decode(outputs.logits.argmax(dim=-1)) |
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return predicted_answer |
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def main(): |
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st.title("Interest Calculation App") |
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app = InterestCalculatorApp() |
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file_path = st.file_uploader("Upload Invoices File", type=["xlsx"]) |
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if file_path is not None: |
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app.load_invoices(file_path) |
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if st.button("Calculate Interest"): |
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app.calculate_interest() |
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app.download_boe_rates() |
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if __name__ == "__main__": |
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main() |
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