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import os
import streamlit as st
import faiss
import numpy as np
import pandas as pd
import plotly.express as px
from sentence_transformers import SentenceTransformer
from groq import Groq
# Load pre-trained embedding model
model = SentenceTransformer("all-MiniLM-L6-v2")
# Load FAISS index
index_path = "faiss_index.idx"
if os.path.exists(index_path):
index = faiss.read_index(index_path)
else:
index = None
# Load dataset (replace with actual datasets)
dataset_path = "electrolysis_data.csv"
if os.path.exists(dataset_path):
df = pd.read_csv(dataset_path)
else:
df = pd.DataFrame() # Empty DataFrame as fallback
# ✅ Set Groq API Key (Replace with your actual key)
os.environ["GROQ_API_KEY"] = "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"
client = Groq(api_key=os.getenv("GROQ_API_KEY"))
# Function for Retrieval-Augmented Generation (RAG)
def rag_pipeline(query):
if index is not None:
query_embedding = model.encode([query]).astype("float32")
D, I = index.search(query_embedding, k=3)
retrieved_docs = [df.iloc[i]["description"] for i in I[0] if i < len(df)]
context = " ".join(retrieved_docs)
else:
context = "No relevant documents found."
# Generate response using Llama 3 (via Groq API)
response = client.chat.completions.create(
messages=[
{"role": "system", "content": f"Context: {context}"},
{"role": "user", "content": query}
],
model="llama3-70b-8192"
)
return response.choices[0].message.content
# Streamlit UI
st.set_page_config(page_title="HydroGen-AI", layout="wide")
st.title("🔬 HydroGen-AI: AI-Powered Hydrogen Techno-Economic Analyzer")
st.markdown("🚀 **Techno-Economic & Simulation-Based Analysis of Hydrogen Production via Electrolysis**")
# Left Panel: User Input
st.sidebar.header("🔧 Input Parameters")
water_source = st.sidebar.selectbox("Water Source", ["Atmospheric", "Seawater", "Groundwater", "Municipal"])
water_cost = st.sidebar.number_input("Water Cost ($/m³)", min_value=0.0, value=0.5)
purification_cost = st.sidebar.number_input("Water Purification Cost ($/m³)", min_value=0.0, value=0.1)
water_quantity = st.sidebar.number_input("Water Quantity (m³)", min_value=0.1, value=10.0)
method = st.sidebar.selectbox("Electrolysis Method", ["Alkaline", "PEM", "SOEC"])
current_density = st.sidebar.number_input("Current Density (A/cm²)", min_value=0.0, value=0.5)
voltage = st.sidebar.number_input("Cell Voltage (V)", min_value=1.0, value=1.8)
temperature = st.sidebar.number_input("Cell Temperature (°C)", min_value=25.0, value=60.0)
membrane = st.sidebar.selectbox("Membrane Type", ["Nafion", "AEM", "Zirfon"])
electrode = st.sidebar.selectbox("Electrode Material", ["Nickel", "Platinum", "Graphite"])
# Analysis Button
if st.sidebar.button("Analyze"):
query = (f"Analyze hydrogen production with {method} electrolysis at {current_density} A/cm², {voltage} V, "
f"{temperature}°C, using {membrane} membrane and {electrode} electrodes. Water: {water_quantity} m³ of {water_source} "
f"(Cost: ${water_cost}, Purification: ${purification_cost}). Provide cost breakdown, efficiency, and production rate.")
result = rag_pipeline(query)
# Display Results
st.subheader("📊 Analysis Result")
st.write(result)
st.success("✅ Analysis completed successfully!")
# Visualization
fig = px.bar(df, x="Parameter", y="Value", title="Techno-Economic Breakdown")
st.plotly_chart(fig)
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