hydrogen_analyzer.py

import streamlit as st
import pandas as pd
import numpy as np
import json
import os
import plotly.express as px
from dotenv import load_dotenv
from groq import Groq

# Load environment variables
load_dotenv()
GROQ_API_KEY = os.getenv('GROQ_API_KEY')

def show_hydrogen_analyzer():
    st.title("🔬 Hydrogen Production Analyzer")
    st.markdown("Analyze and optimize your hydrogen production process.")

    # Sidebar inputs
    st.sidebar.subheader("🔧 Input Parameters")
    water_source = st.sidebar.selectbox("Water Source", ["Tap Water", "Deionized", "Seawater"])
    production_method = st.sidebar.selectbox("Production Method", ["Alkaline", "PEM", "SOEC"])
    current_density = st.sidebar.slider("Current Density (A/cm²)", 0.1, 2.0, 0.5)
    voltage = st.sidebar.slider("Voltage (V)", 1.4, 5.0, 2.0)
    energy_source = st.sidebar.selectbox("Energy Source", ["Grid", "Solar", "Wind"])

    # Analysis Button
    if st.button("Analyze Hydrogen Production"):
        # Mock calculation
        production_rate = np.round(current_density * voltage * 10, 2)  # Dummy formula
        cost_per_kg = np.round(10 / production_rate, 2) if production_rate else 0

        # Display Results
        st.metric("⚡ Production Rate", f"{production_rate} g/hour")
        st.metric("💰 Cost per kg H₂", f"${cost_per_kg}")

        # AI Optimization (Mock)
        ai_recommendations = {
            "Efficiency Boost": "Increase voltage to 2.5V",
            "Cost Reduction": "Use renewable energy sources",
            "Best Electrolyzer": "PEM recommended"
        }

        st.subheader("🤖 AI Recommendations")
        st.json(ai_recommendations)