Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,196 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+from pricer import (
+    Product,
+    suggest_price,
+    freshness_factor,
+    format_sms,
+    simulate_7_days
+)
+
+# -----------------------------
+# LOAD TRAINED ML MODEL
+# -----------------------------
+ml_model = joblib.load("demand_model.pkl")
+
+def ml_predict(price, age_hours, comp_avg, product_encoded):
+    X = pd.DataFrame([[
+        price,
+        age_hours,
+        comp_avg,
+        product_encoded
+    ]], columns=["price", "age_hours", "comp_avg_price", "product_encoded"])
+
+    return ml_model.predict(X)[0]
+
+# -----------------------------
+# PAGE CONFIG
+# -----------------------------
+st.set_page_config(page_title="AI Hybrid Pricing Engine", layout="wide")
+
+st.title("🧠 AI Hybrid Retail Pricing System")
+st.markdown("Rule-based + Machine Learning Dynamic Pricing Engine")
+
+# -----------------------------
+# SIDEBAR INPUTS
+# -----------------------------
+st.sidebar.header("📦 Product Configuration")
+
+sku = st.sidebar.text_input("Product SKU", "TOMATO-A")
+cost = st.sidebar.number_input("Cost (RWF)", 500, 10000, 1000)
+shelf_life = st.sidebar.number_input("Shelf Life (days)", 1, 14, 7)
+
+p_ref = st.sidebar.number_input("Reference Price", 1000, 10000, 1800)
+Q0 = st.sidebar.number_input("Base Demand (Q0)", 10, 500, 50)
+alpha = st.sidebar.slider("Price Sensitivity (alpha)", 0.1, 5.0, 1.5)
+
+age = st.sidebar.slider("Product Age (days)", 0.0, float(shelf_life), 3.0)
+
+# Competitors
+comp1 = st.sidebar.number_input("Competitor 1", 1000, 10000, 1600)
+comp2 = st.sidebar.number_input("Competitor 2", 1000, 10000, 1700)
+comp3 = st.sidebar.number_input("Competitor 3", 1000, 10000, 1900)
+
+competitors = [comp1, comp2, comp3]
+
+# -----------------------------
+# PRODUCT OBJECT
+# -----------------------------
+product = Product(
+    sku=sku,
+    cost=cost,
+    shelf_life_days=shelf_life,
+    p_ref=p_ref,
+    Q0=Q0,
+    alpha=alpha
+)
+
+# -----------------------------
+# RULE-BASED RESULT
+# -----------------------------
+result = suggest_price(product, age, competitors)
+
+# -----------------------------
+# DISPLAY METRICS
+# -----------------------------
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.metric("🧊 Freshness", f"{result['freshness']:.3f}")
+    st.write(result["freshness_label"])
+
+with col2:
+    st.metric("💰 Rule Price", f"{result['suggested_price']:.0f} RWF")
+
+with col3:
+    st.metric("📈 Margin %", f"{result['margin_pct']:.1f}%")
+
+st.divider()
 
-"""
-# Welcome to Streamlit!
+# -----------------------------
+# SMS OUTPUT
+# -----------------------------
+st.subheader("📲 SMS Output")
+st.code(format_sms(result, "RWF"))
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# -----------------------------
+# FRESHNESS CURVE
+# -----------------------------
+st.subheader("📉 Freshness Decay Curve")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+days = np.linspace(0, shelf_life, 50)
+values = [freshness_factor(d, shelf_life) for d in days]
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+df_curve = pd.DataFrame({
+    "Day": days,
+    "Freshness": values
+})
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+st.line_chart(df_curve.set_index("Day"))
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+# -----------------------------
+# COMPETITOR VIEW
+# -----------------------------
+st.subheader("🏪 Market Comparison")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
+df_comp = pd.DataFrame({
+    "Entity": ["Competitor 1", "Competitor 2", "Competitor 3", "YOU (Rule)"],
+    "Price (RWF)": [comp1, comp2, comp3, result["suggested_price"]]
 })
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.dataframe(df_comp)
+
+# -----------------------------
+# ML VS RULE COMPARISON
+# -----------------------------
+st.subheader("⚖️ Rule Engine vs ML Model")
+
+comp_avg = np.mean(competitors)
+product_encoded = 0
+
+rule_profit = result["expected_daily_profit"]
+
+ml_demand = ml_predict(
+    result["suggested_price"],
+    age * 24,
+    comp_avg,
+    product_encoded
+)
+
+ml_profit = (result["suggested_price"] - cost) * ml_demand
+
+col1, col2 = st.columns(2)
+
+with col1:
+    st.metric("📏 Rule Profit", f"{rule_profit:.2f} RWF")
+
+with col2:
+    st.metric("🤖 ML Profit", f"{ml_profit:.2f} RWF")
+
+# -----------------------------
+# PROFIT CURVE COMPARISON
+# -----------------------------
+st.subheader("📊 Profit Curve Comparison")
+
+prices = np.linspace(500, 5000, 30)
+
+ml_profits = []
+rule_profits = []
+
+for p in prices:
+    ml_d = ml_predict(p, age * 24, comp_avg, product_encoded)
+    ml_profits.append((p - cost) * ml_d)
+
+    rule_profits.append(result["expected_daily_profit"])
+
+df_compare = pd.DataFrame({
+    "Price": prices,
+    "ML_Profit": ml_profits,
+    "Rule_Profit": rule_profits
+})
+
+st.line_chart(df_compare.set_index("Price"))
+
+# -----------------------------
+# 7-DAY SIMULATION
+# -----------------------------
+st.subheader("📊 7-Day Simulation")
+
+if st.button("Run Simulation"):
+
+    sim = simulate_7_days(product, competitors)
+    df_sim = pd.DataFrame(sim)
+
+    if "day" in df_sim.columns:
+        st.line_chart(df_sim.set_index("day")[["our_price", "demand"]])
+
+    st.dataframe(df_sim)
+
+# -----------------------------
+# FOOTER
+# -----------------------------
+st.markdown("---")
+st.markdown("⚡ Hybrid AI Pricing System — Rule Engine + Machine Learning | AIMS-RIC Hackathon")