Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+
+# Sample Data for Testing
+competitor_prices = pd.DataFrame({
+    "Competitor": ["Shop A", "Shop B", "Shop C", "Shop D"],
+    "Product": ["Sneakers", "Hoodies", "Watches", "Backpacks"],
+    "Price (Ksh)": [4500, 3200, 7500, 2800],
+    "Discount (%)": [10, 5, 15, 0]
+})
+
+sales_data = pd.DataFrame({
+    "Date": pd.date_range(start="2024-01-01", periods=30, freq="D"),
+    "Sales (Ksh)": np.random.randint(5000, 50000, size=30)
+})
+
+# ---- Streamlit App ----
+st.set_page_config(page_title="AI Business Dashboard", layout="wide")
+
+# ---- Sidebar Navigation ----
+st.sidebar.title("📊 AI Business Intelligence")
+page = st.sidebar.radio("Navigate", ["Dashboard", "Competitor Analysis", "Marketing Insights", "AI Suggestions"])
+
+# ---- Dashboard ----
+if page == "Dashboard":
+    st.title("📈 Business Performance Dashboard")
+    
+    tab1, tab2 = st.tabs(["Sales Trends", "Product Insights"])
+    
+    with tab1:
+        st.subheader("📊 Daily Sales Performance")
+        fig = px.line(sales_data, x="Date", y="Sales (Ksh)", title="Sales Over Time")
+        st.plotly_chart(fig, use_container_width=True)
+
+    with tab2:
+        st.subheader("📦 Top-Selling Products")
+        top_products = pd.DataFrame({
+            "Product": ["Sneakers", "Hoodies", "Watches", "Backpacks"],
+            "Sales (Ksh)": [25000, 18000, 15000, 12000]
+        })
+        fig2 = px.bar(top_products, x="Product", y="Sales (Ksh)", title="Best-Selling Products", text="Sales (Ksh)")
+        st.plotly_chart(fig2, use_container_width=True)
+
+# ---- Competitor Analysis ----
+elif page == "Competitor Analysis":
+    st.title("📉 Competitor Price Tracking")
+    st.table(competitor_prices)
+    
+    # Price Adjustment Recommendation
+    st.subheader("💡 AI Pricing Suggestion")
+    st.info("Your Sneakers are priced at Ksh 5000. AI suggests a **10% discount (Ksh 4500)** to remain competitive.")
+
+# ---- Marketing Insights ----
+elif page == "Marketing Insights":
+    st.title("📣 Social Media Performance")
+    tab1, tab2 = st.tabs(["Instagram Engagement", "Ad Optimization"])
+    
+    with tab1:
+        st.subheader("🔥 Best Posting Times")
+        best_times = pd.DataFrame({"Time": ["10 AM", "1 PM", "6 PM", "9 PM"], "Engagement": [300, 450, 700, 550]})
+        fig3 = px.bar(best_times, x="Time", y="Engagement", title="Best Times to Post on Instagram")
+        st.plotly_chart(fig3, use_container_width=True)
+    
+    with tab2:
+        st.subheader("📢 Ad Performance Insights")
+        st.write("💡 AI suggests allocating **60% of the budget** to Instagram Stories instead of Feed Ads for better ROI.")
+
+# ---- AI Suggestions ----
+elif page == "AI Suggestions":
+    st.title("🤖 AI-Powered Business Recommendations")
+    st.info("📈 AI predicts that **Hoodies will trend next month**. Consider increasing stock and running a promo.")
+    st.info("💰 AI suggests increasing ad spend on **Instagram Reels for better conversions**.")
+
+# ---- Footer ----
+st.sidebar.markdown("---")
+st.sidebar.caption("🚀 Powered by Waziri Collective Labs | 2025")