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import os
import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from groq import Groq
# --------------------------
# Set your Groq API key manually (for Colab)
# --------------------------
# You can either:
# 1️⃣ Use environment variable:
os.environ["GROQ_API_KEY"] = "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"
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
# --------------------------
# Initialize Groq client
# --------------------------
if not GROQ_API_KEY:
st.warning("⚠️ Please set your GROQ_API_KEY in Colab before running.")
client = Groq(api_key=GROQ_API_KEY)
# --------------------------
# Streamlit App Config
# --------------------------
st.set_page_config(page_title="AI Expense Analyzer", page_icon="💰", layout="wide")
st.title("💰 AI Expense Analyzer")
st.markdown("Track your expenses, visualize spending patterns, and get smart AI suggestions!")
# --------------------------
# Initialize Session State
# --------------------------
if "expenses" not in st.session_state:
st.session_state.expenses = pd.DataFrame(columns=["Date", "Category", "Amount", "Description"])
# --------------------------
# Expense Input Form
# --------------------------
with st.expander("➕ Add New Expense", expanded=True):
with st.form("expense_form"):
date = st.date_input("Date")
category = st.selectbox("Category", ["Food", "Transport", "Shopping", "Entertainment", "Bills", "Other"])
amount = st.number_input("Amount (in PKR)", min_value=0.0, step=100.0)
description = st.text_area("Description (optional)")
submitted = st.form_submit_button("Add Expense")
if submitted:
new_data = pd.DataFrame([[date, category, amount, description]],
columns=["Date", "Category", "Amount", "Description"])
st.session_state.expenses = pd.concat([st.session_state.expenses, new_data], ignore_index=True)
st.success("✅ Expense added successfully!")
# --------------------------
# Display Expenses
# --------------------------
if not st.session_state.expenses.empty:
st.subheader("📊 Expense Records")
st.dataframe(st.session_state.expenses, use_container_width=True)
# Pie Chart
st.subheader("🧩 Expense Distribution by Category")
category_data = st.session_state.expenses.groupby("Category")["Amount"].sum()
fig1, ax1 = plt.subplots()
ax1.pie(category_data, labels=category_data.index, autopct="%1.1f%%", startangle=90)
ax1.axis("equal")
st.pyplot(fig1)
# Trend Chart
st.subheader("📈 Spending Trend Over Time")
trend_data = st.session_state.expenses.groupby("Date")["Amount"].sum().reset_index()
fig2, ax2 = plt.subplots()
ax2.plot(trend_data["Date"], trend_data["Amount"], marker="o")
ax2.set_xlabel("Date")
ax2.set_ylabel("Total Spending (PKR)")
ax2.set_title("Spending Trend")
st.pyplot(fig2)
# AI Analysis
st.subheader("🤖 AI Expense Insights")
user_expense_summary = st.session_state.expenses.to_string(index=False)
if st.button("Analyze My Expenses 💬"):
with st.spinner("Analyzing your expense patterns with Groq Llama 3.3..."):
try:
prompt = f"""
You are a financial advisor. Analyze the following expense data and identify:
1. Spending patterns
2. Categories with overspending
3. Suggested saving strategies
4. A short summary of financial health.
Expense Data:
{user_expense_summary}
"""
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama-3.3-70b-versatile",
)
ai_analysis = chat_completion.choices[0].message.content
st.success("✅ Analysis Complete!")
st.markdown(ai_analysis)
except Exception as e:
st.error(f"❌ Error: {str(e)}")
else:
st.info("💡 Add some expenses above to get started!")
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