Update app.py

app.py

@@ -12,7 +12,7 @@ def main():
 
     if api_key:
         # Download BOE rates
-        download_boe_rates()
+        boe_rates_df = download_boe_rates()
 
         # Allow user to upload Excel sheet
         uploaded_file = st.file_uploader("Upload Excel file", type=["xlsx", "xls"])
@@ -40,7 +40,7 @@ def main():
                 })
 
                 # Calculate late interest
-                df_with_interest = calculate_late_interest(df_calculate, late_interest_rate)
+                df_with_interest = calculate_late_interest(df_calculate, late_interest_rate, boe_rates_df)
 
                 # Display calculated late interest
                 total_late_interest = df_with_interest['late_interest'].sum()
@@ -48,14 +48,7 @@ def main():
                 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?"
+            prompt = generate_conversation_prompt(df, boe_rates_df)
 
             # Allow user to engage in conversation
             user_input = st.text_input("Start a conversation:")
@@ -68,7 +61,7 @@ def main():
                         {"role": "system", "content": prompt},
                         {"role": "user", "content": user_input}
                     ],
-                    max_tokens=1800 
+                    max_tokens=1800  # Adjust this value to allow longer responses
                 )
                 response = completion.choices[0].message['content']
                 st.write("AI's Response:")
@@ -76,18 +69,50 @@ def main():
     else:
         st.warning("Please enter your OpenAI API key.")
 
+# Function to generate conversation prompt
+def generate_conversation_prompt(df, boe_rates_df):
+    prompt = "I have analyzed the provided Excel sheet. "
+
+    # Include due dates, payment dates, and amounts from the Excel sheet
+    due_dates = df['due_date'].tolist()
+    payment_dates = df['payment_date'].tolist()
+    amounts = df['amount'].tolist()
+    prompt += f"The due dates in the sheet are: {', '.join(str(date) for date in due_dates)}. "
+    prompt += f"The payment dates in the sheet are: {', '.join(str(date) for date in payment_dates)}. "
+    prompt += f"The amounts in the sheet are: {', '.join(str(amount) for amount in amounts)}. "
+
+    # Include Bank of England base rates
+    if boe_rates_df is not None:
+        prompt += "The Bank of England base rates are as follows: \n"
+        for index, row in boe_rates_df.iterrows():
+            prompt += f"On {row['Date Changed']}, the base rate was {row['Current Bank Rate']}. \n"
+
+    prompt += "Based on this information, what would you like to discuss?"
+
+    return prompt
+
 # Function to calculate late interest
-def calculate_late_interest(data, late_interest_rate):
+def calculate_late_interest(data, late_interest_rate, boe_rates_df):
+    # Convert due_date column to Timestamp objects
+    data['due_date'] = pd.to_datetime(data['due_date'])
+    data['payment_date'] = pd.to_datetime(data['payment_date'])
+
     # 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)
+
+    # Consider additional factors like Bank of England base rate
+    if boe_rates_df is not None:
+        data['boe_base_rate'] = data['due_date'].map(lambda x: get_boe_base_rate(x, boe_rates_df))
+        data['late_interest'] += data['amount'] * (data['boe_base_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()
+    due_dates = pd.to_datetime(df.iloc[:, 0], errors='coerce').dropna()  # Convert to datetime and drop NaT values
+    payment_dates = pd.to_datetime(df.iloc[:, 1], errors='coerce').dropna()  # Convert to datetime and drop NaT values
     amounts = []
 
     # Extract and clean amounts from third column
@@ -111,10 +136,18 @@ def download_boe_rates():
             df = pd.read_html(response.text)[0]
             df.to_csv('boe_rates.csv', index=False)
             st.success("Bank of England rates downloaded successfully.")
+            return df  # Return the downloaded data
         else:
             st.error("Failed to retrieve data from the Bank of England website.")
+            return None
     except requests.RequestException as e:
         st.error(f"Failed to download rates: {e}")
+        return None
+
+def get_boe_base_rate(date, boe_rates_df):
+    closest_date_index = (boe_rates_df['Date Changed'] - pd.Timestamp(date)).abs().argsort()[0]
+    closest_date = boe_rates_df['Date Changed'].iloc[closest_date_index]
+    return boe_rates_df.loc[closest_date_index, 'Current Bank Rate']
 
 if __name__ == "__main__":
     main()