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import streamlit as st
import pandas as pd
import openai
# Streamlit App
def main():
st.title("Invoice Interest Calculator and Conversation")
# Prompt user for OpenAI API key
api_key = st.text_input("Enter your OpenAI API key:")
if api_key:
# Allow user to upload Excel sheet
uploaded_file = st.file_uploader("Upload Excel file", type=["xlsx", "xls"])
if uploaded_file is not None:
df = pd.read_excel(uploaded_file)
# Display uploaded data
st.write("Uploaded Data:")
st.write(df)
# Analyze Excel sheet
due_dates, payment_dates, amounts = analyze_excel(df)
# Allow user to specify late interest rate
late_interest_rate = st.number_input("Enter Late Interest Rate (%):", min_value=0.0, max_value=100.0, step=0.1)
# Calculate late interest if due dates and payment dates are available
if due_dates and payment_dates:
# Create DataFrame with extracted due dates, payment dates, and placeholder amount
df_calculate = pd.DataFrame({
'due_date': due_dates,
'payment_date': payment_dates,
'amount': amounts
})
# Calculate late interest
df_with_interest = calculate_late_interest(df_calculate, late_interest_rate)
# Display calculated late interest
total_late_interest = df_with_interest['late_interest'].sum()
st.write("Calculated Late Interest:")
st.write(total_late_interest)
# Generate conversation prompt
prompt = "I have analyzed the provided Excel sheet. "
if due_dates:
prompt += f"The due dates in the sheet are: {', '.join(str(date) for date in due_dates)}. "
if payment_dates:
prompt += f"The payment dates in the sheet are: {', '.join(str(date) for date in payment_dates)}. "
if amounts:
prompt += f"The amounts in the sheet are: {', '.join(str(amount) for amount in amounts)}. "
prompt += "Based on this information, what would you like to discuss?"
# Allow user to engage in conversation
user_input = st.text_input("Start a conversation:")
if st.button("Send"):
openai.api_key = api_key # Set user-provided OpenAI API key
completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": user_input}
],
max_tokens=1800 # Adjust this value to allow longer responses
)
response = completion.choices[0].message['content']
st.write("AI's Response:")
st.write(response)
else:
st.warning("Please enter your OpenAI API key.")
# Function to calculate late interest
def calculate_late_interest(data, late_interest_rate):
# Calculate late days and late interest
data['late_days'] = (data['payment_date'] - data['due_date']).dt.days.clip(lower=0)
data['late_interest'] = data['late_days'] * data['amount'] * (late_interest_rate / 100)
return data
# Function to analyze Excel sheet and extract relevant information
def analyze_excel(df):
# Extract due dates and payment dates
due_dates = df.iloc[:, 0].dropna().tolist()
payment_dates = df.iloc[:, 1].dropna().tolist()
amounts = []
# Extract and clean amounts from third column
for amount in df.iloc[:, 2]:
if isinstance(amount, str):
amount = amount.replace('"', '').replace(',', '')
amounts.append(float(amount))
return due_dates, payment_dates, amounts
if __name__ == "__main__":
main()
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