Create app.py

app.py

@@ -0,0 +1,203 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import json
+import os
+from groq import Groq
+from dotenv import load_dotenv
+import plotly.express as px
+import plotly.graph_objects as go
+from datetime import datetime
+
+# Load environment variables
+load_dotenv()
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+
+# Custom CSS to make the page full-windowed
+st.markdown(
+    """
+    <style>
+    .stApp {
+        max-width: 100%;
+        padding: 0;
+    }
+    .stButton>button {
+        width: 100%;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Helper functions for calculations (from previous_app.py)
+def calculate_h2_production(method, water_quantity, energy_input, current_density, voltage):
+    """Calculate hydrogen production based on input parameters"""
+    method_efficiencies = {
+        "Alkaline Electrolysis": 0.65,
+        "PEM Electrolysis": 0.75,
+        "SOEC": 0.85
+    }
+    faraday_constant = 96485  # C/mol
+    molar_mass_h2 = 2.02  # g/mol
+    efficiency = method_efficiencies[method]
+    surface_area = water_quantity * 0.1
+    current = current_density * surface_area
+    time_hours = energy_input / (voltage * current)
+    moles_h2 = (current * time_hours * 3600 * efficiency) / (2 * faraday_constant)
+    mass_h2 = moles_h2 * molar_mass_h2
+    volume_h2 = moles_h2 * 22.4
+    return {
+        "production_rate_g_per_hour": mass_h2 / time_hours,
+        "total_production_g": mass_h2,
+        "total_production_L": volume_h2,
+        "efficiency": efficiency,
+        "operation_time_hours": time_hours
+    }
+
+def calculate_cost(method, water_cost, water_purification_cost, energy_source, energy_input, h2_production):
+    """Calculate the cost of hydrogen production"""
+    energy_costs = {
+        "Grid Electricity": 0.12,
+        "Solar": 0.08,
+        "Wind": 0.06,
+        "Nuclear": 0.10,
+        "Hydroelectric": 0.07
+    }
+    operational_costs = {
+        "Alkaline Electrolysis": 1.2,
+        "PEM Electrolysis": 1.5,
+        "SOEC": 1.8
+    }
+    total_water_cost = water_cost * (h2_production["total_production_g"] / 1000)
+    total_purification_cost = water_purification_cost * (h2_production["total_production_g"] / 1000)
+    energy_cost_rate = energy_costs[energy_source]
+    total_energy_cost = energy_cost_rate * energy_input
+    operational_cost_rate = operational_costs[method]
+    total_operational_cost = operational_cost_rate * (h2_production["total_production_g"] / 1000)
+    total_cost = total_water_cost + total_purification_cost + total_energy_cost + total_operational_cost
+    cost_per_kg = total_cost / (h2_production["total_production_g"] / 1000) if h2_production["total_production_g"] > 0 else 0
+    return {
+        "water_cost": total_water_cost,
+        "purification_cost": total_purification_cost,
+        "energy_cost": total_energy_cost,
+        "operational_cost": total_operational_cost,
+        "total_cost": total_cost,
+        "cost_per_kg": cost_per_kg
+    }
+
+def call_groq_api(user_inputs, production_data, cost_data):
+    """Call Groq API with Llama 3 to analyze production parameters and provide recommendations"""
+    try:
+        client = Groq(api_key=os.environ.get("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"))
+    except Exception as e:
+        return {"error": f"Failed to initialize Groq client: {str(e)}"}
+    
+    prompt = f"""
+    As a hydrogen production expert, analyze the following electrolysis parameters and provide recommendations for optimization:
+    
+    Input Parameters:
+    - Water Source: {user_inputs['water_source']}
+    - Production Method: {user_inputs['production_method']}
+    - Energy Source: {user_inputs['energy_source']}
+    - Current Density: {user_inputs['current_density']} A/cm²
+    - Voltage: {user_inputs['voltage']} V
+    - Membrane Material: {user_inputs['membrane']}
+    - Electrode Materials: {user_inputs['electrodes']}
+    
+    Production Results:
+    - Production Rate: {production_data['production_rate_g_per_hour']:.2f} g/hour
+    - Total Production: {production_data['total_production_g']:.2f} g
+    - Efficiency: {production_data['efficiency'] * 100:.1f}%
+    - Operation Time: {production_data['operation_time_hours']:.2f} hours
+    
+    Cost Analysis:
+    - Water Cost: ${cost_data['water_cost']:.2f}
+    - Purification Cost: ${cost_data['purification_cost']:.2f}
+    - Energy Cost: ${cost_data['energy_cost']:.2f}
+    - Operational Cost: ${cost_data['operational_cost']:.2f}
+    - Total Cost: ${cost_data['total_cost']:.2f}
+    - Cost per kg H₂: ${cost_data['cost_per_kg']:.2f}
+    
+    Please provide:
+    1. An efficiency assessment of the current setup
+    2. Three specific recommendations to improve efficiency
+    3. Three specific recommendations to reduce costs
+    4. An ideal parameter configuration based on the provided inputs
+    
+    Format your response as a structured JSON with these fields:
+    {
+        "efficiency_assessment": "text analysis",
+        "efficiency_recommendations": ["recommendation1", "recommendation2", "recommendation3"],
+        "cost_recommendations": ["recommendation1", "recommendation2", "recommendation3"],
+        "ideal_parameters": {
+            "current_density": value,
+            "voltage": value,
+            "membrane": "recommendation",
+            "electrodes": "recommendation",
+            "energy_source": "recommendation"
+        },
+        "estimated_improvement": {
+            "efficiency_increase": "percentage",
+            "cost_reduction": "percentage"
+        }
+    }
+    """
+    
+    try:
+        chat_completion = client.chat.completions.create(
+            messages=[{"role": "user", "content": prompt}],
+            model="llama-3.3-70b-versatile",
+            temperature=0.5,
+            max_tokens=1024,
+            response_format={"type": "json_object"}
+        )
+        response_content = chat_completion.choices[0].message.content
+        return json.loads(response_content)
+    except Exception as e:
+        return {"error": f"Error calling Groq API: {str(e)}"}
+
+# Function to display the app interface
+def show_app_interface():
+    """Function to display the app interface"""
+    st.title("Hydrogen Production Analysis & Optimization")
+    st.write("This is the app interface.")
+
+# Function to display the AI chatbot
+def show_chatbot():
+    """Function to display the AI chatbot"""
+    st.title("AI Chatbot")
+    user_input = st.text_input("Ask me anything about hydrogen production:")
+    if user_input:
+        response = call_groq_api({}, {}, {})
+        st.write(response)
+
+# Function to display the landing page
+def show_landing_page():
+    """Function to display the landing page"""
+    st.markdown(
+        """
+        <div style="text-align: center; padding: 50px;">
+            <h1>Ready to Transform Your Hydrogen Projects?</h1>
+            <p>Join the hydrogen revolution with AI-powered techno-economic analysis that gives you the competitive edge.</p>
+            <button style="padding: 10px 20px; margin: 10px; background-color: #4CAF50; color: white; border: none; border-radius: 5px;" onclick="window.location.href='?page=app'">Request a Demo</button>
+            <button style="padding: 10px 20px; margin: 10px; background-color: #1E88E5; color: white; border: none; border-radius: 5px;" onclick="window.location.href='?page=chatbot'">Learn More</button>
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Main function to handle navigation
+def main():
+    """Main function to handle navigation between pages"""
+    query_params = st.experimental_get_query_params()
+    page = query_params.get("page", ["landing"])[0]
+
+    if page == "app":
+        show_app_interface()
+    elif page == "chatbot":
+        show_chatbot()
+    else:
+        show_landing_page()
+
+if __name__ == "__main__":
+    main()