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import streamlit as st
import pdfplumber
import pandas as pd
# Function to classify transactions based on description
def classify_transaction(description):
if not isinstance(description, str): # Ensure description is a string
return "Unknown"
categories = {
"Grocery": ["Walmart", "Kroger", "Whole Foods"],
"Dining": ["McDonald's", "Starbucks", "Chipotle"],
"Bills": ["Verizon", "AT&T", "Con Edison"],
"Entertainment": ["Netflix", "Spotify", "Amazon Prime"],
"Transport": ["Uber", "Lyft", "MetroCard"],
}
for category, keywords in categories.items():
if any(keyword in description for keyword in keywords):
return category
return "Other"
# Function to process the uploaded PDF and classify transactions
def process_pdf(file):
if file is None:
st.error("No file uploaded.")
return None
# Extract text from PDF
with pdfplumber.open(file) as pdf:
text = "\n".join([page.extract_text() for page in pdf.pages if page.extract_text()])
# Extract transactions (Modify based on your statement format)
lines = text.split("\n")
transactions = [line for line in lines if any(char.isdigit() for char in line)]
# Convert to DataFrame
df = pd.DataFrame([line.split()[:3] for line in transactions], columns=["Date", "Description", "Amount"])
# Ensure no missing descriptions
df["Description"] = df["Description"].fillna("Unknown")
# Apply classification
df["Category"] = df["Description"].apply(classify_transaction)
return df # Return DataFrame
# Streamlit UI
st.title("📄 Credit Card Statement Classifier")
st.write("Upload a **PDF bank/credit card statement** to categorize transactions automatically.")
uploaded_file = st.file_uploader("Upload PDF", type=["pdf"])
if uploaded_file is not None:
st.success("✅ File uploaded successfully!")
# Process and display transactions
df_result = process_pdf(uploaded_file)
if df_result is not None:
st.write("### 📊 Classified Transactions:")
st.dataframe(df_result) # Display table
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