app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from data_fetcher import get_hydrogen_data
from model import predict_hydrogen_production

# Load dataset
df = get_hydrogen_data()

# Streamlit UI
st.set_page_config(page_title="Hydrogen Analysis App", layout="wide")

# Sidebar Inputs
st.sidebar.header("Electrolysis Parameters")
current_density = st.sidebar.slider("Current Density (A/cm²)", 0.1, 2.0, 1.0)
voltage = st.sidebar.slider("Voltage (V)", 1.6, 2.2, 1.8)
temperature = st.sidebar.slider("Cell Temperature (°C)", 25, 80, 50)
membrane = st.sidebar.selectbox("Membrane Type", ["Nafion", "Fumapem", "Other"])
electrodes = st.sidebar.selectbox("Electrodes", ["Platinum", "Nickel", "Other"])

# Predict Button
if st.sidebar.button("Predict Hydrogen Production"):
    params = {
        "current_density": current_density,
        "voltage": voltage,
        "temperature": temperature,
        "membrane": membrane,
        "electrodes": electrodes
    }
    
    prediction = predict_hydrogen_production(params)
    st.sidebar.success(f"🔍 Prediction:\n{prediction}")

# Dashboard
st.title("🚀 Hydrogen Techno-Economics Analysis")
st.markdown("This app provides insights into hydrogen production via electrolysis.")

# Show Dataset
st.subheader("📊 Electrolysis Data")
st.dataframe(df)

# Comparison Graphs
st.subheader("📉 Efficiency Comparison")
fig, ax = plt.subplots()
sns.barplot(x=df["Parameter"], y=df["Value"], ax=ax, palette="coolwarm")
plt.xticks(rotation=30, ha="right")
st.pyplot(fig)

# Cost Analysis
st.subheader("💰 Cost Analysis")
water_cost = df[df["Parameter"] == "Water Cost"]["Value"].values[0]
energy_cost = df[df["Parameter"] == "Energy Cost"]["Value"].values[0]
hydrogen_rate = df[df["Parameter"] == "Hydrogen Production Rate"]["Value"].values[0]

st.metric("Water Cost", f"${water_cost} per m³")
st.metric("Energy Cost", f"${energy_cost} per kWh")
st.metric("Hydrogen Production", f"{hydrogen_rate} ml/min")

# Conclusion
st.subheader("🔍 Interpretation & Suggestions")
st.info("Increase voltage and optimize membrane to improve efficiency.")