Update app.py

app.py

@@ -1,7 +1,76 @@
 import streamlit as st
 import pandas as pd
 import openai
-from dateutil import parser
+
+# 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):
@@ -25,70 +94,5 @@ def analyze_excel(df):
 
     return due_dates, payment_dates, amounts
 
-# Streamlit App
-def main():
-    st.title("Invoice Interest Calculator and Conversation")
-
-    # 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': [0] * len(due_dates)  # Placeholder amount for calculation
-            })
-
-            # Calculate late interest
-            df_with_interest = calculate_late_interest(df_calculate, late_interest_rate)
-
-            # Display calculated late interest
-            st.write("Calculated Late Interest:")
-            st.write(df_with_interest['late_interest'].sum())
-
-        # 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"):
-            if 'api_key' not in st.session_state:
-                st.session_state.api_key = st.text_input("Enter your OpenAI API key:")
-            openai.api_key = st.session_state.api_key  # Set OpenAI API key
-            
-            completion = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=[
-                    {"role": "system", "content": prompt},
-                    {"role": "user", "content": user_input}
-                ],
-                max_tokens=800  # Adjust this value to allow longer responses
-            )
-            response = completion.choices[0].message['content']
-            st.write("AI's Response:")
-            st.write(response)
-
 if __name__ == "__main__":
     main()