Menausar/ExponentialGD

1import streamlit as st2import numpy as np3import pandas as pd4import matplotlib.pyplot as plt5from io import BytesIO6 7st.set_page_config(page_title="Exponential Growth & Decay Applets", layout="wide")8 9st.title("📈 Exponential Growth & Decay Interactive Applets")10st.write("Select a student scenario, adjust parameters, and explore exponential models.")11 12# -----------------------------13# Utility Functions14# -----------------------------15def exponential_model(initial, rate, time):16    return initial * np.exp(rate * time)17 18def export_plot(fig):19    buf = BytesIO()20    fig.savefig(buf, format="png")21    buf.seek(0)22    return buf23 24# -----------------------------25# Scenario Selector26# -----------------------------27scenarios = {28    "Energy / Battery Decay (Cheer, Robotics, Gaming, Submersibles)": "decay",29    "Savings, Revenue, Attendance, Views Growth": "growth",30    "Heart Rate / Recovery / Fatigue": "decay",31    "Bacteria / Germ Growth & Treatment": "both",32    "Cooling / Heat Decay": "decay",33    "Skill Improvement (Accuracy, Efficiency)": "growth",34}35 36scenario = st.selectbox("Choose an Applet Scenario", list(scenarios.keys()))37 38# -----------------------------39# Sidebar Controls40# -----------------------------41st.sidebar.header("🔧 Model Parameters")42 43initial = st.sidebar.slider("Initial Value", 1.0, 1000.0, 100.0)44rate = st.sidebar.slider(45    "Growth (+) or Decay (–) Rate",46    -2.0, 2.0, -0.3 if scenarios[scenario] == "decay" else 0.3,47    step=0.0148)49time_max = st.sidebar.slider("Time Duration", 1, 100, 30)50 51time = np.linspace(0, time_max, 300)52 53# -----------------------------54# Model Calculation55# -----------------------------56values = exponential_model(initial, rate, time)57 58# Optional comparison model (used for bacteria, treatment, cleaning, etc.)59comparison = None60if scenarios[scenario] == "both":61    treatment_rate = st.sidebar.slider("Treatment / Cleaning Effectiveness", -3.0, -0.1, -1.0)62    comparison = exponential_model(initial, treatment_rate, time)63 64# -----------------------------65# Plot66# -----------------------------67fig, ax = plt.subplots()68 69ax.plot(time, values, label="Exponential Model", linewidth=2)70 71if comparison is not None:72    ax.plot(time, comparison, linestyle="--", label="With Treatment / Cleaning")73 74ax.set_xlabel("Time")75ax.set_ylabel("Quantity")76ax.set_title("Exponential Growth & Decay Model")77ax.legend()78ax.grid(True)79 80st.pyplot(fig)81 82# -----------------------------83# Data Table84# -----------------------------85data = pd.DataFrame({86    "Time": time,87    "Value": values88})89 90if comparison is not None:91    data["With Treatment"] = comparison92 93st.subheader("📊 Model Data")94st.dataframe(data.head(10))95 96# -----------------------------97# Export Section98# -----------------------------99st.subheader("⬇️ Export Results")100 101col1, col2 = st.columns(2)102 103with col1:104    csv = data.to_csv(index=False).encode("utf-8")105    st.download_button(106        label="Download Data (CSV)",107        data=csv,108        file_name="exponential_model_data.csv",109        mime="text/csv"110    )111 112with col2:113    img_buf = export_plot(fig)114    st.download_button(115        label="Download Graph (PNG)",116        data=img_buf,117        file_name="exponential_model_graph.png",118        mime="image/png"119    )120 121st.success("Adjust sliders to explore different real-world exponential scenarios!")