Create app.py

app.py

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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!")