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import streamlit as st
import tempfile
import wntr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
@st.cache_resource
def load_llm():
model_name = "deepseek-ai/deepseek-coder-6.7b-instruct" # You can swap this with another DeepSeek model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", trust_remote_code=True)
return pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)
llm = load_llm()
st.title("💧 EPANET + WNTR + DeepSeek LLM Assistant")
uploaded_file = st.file_uploader("Upload your EPANET .inp file", type=["inp"])
wn = None
if uploaded_file:
with tempfile.NamedTemporaryFile(delete=False, suffix=".inp") as tmp_file:
tmp_file.write(uploaded_file.read())
inp_path = tmp_file.name
wn = wntr.network.WaterNetworkModel(inp_path)
st.success("Water network model loaded successfully.")
question = st.text_input("Ask a question about your water network model")
if st.button("Generate Python Code") and wn and question:
prompt = f"""
You are a Python expert using the WNTR library for water network simulations.
Given a WNTR water network model `wn`, generate a Python function called `answer()` that answers this question:
Question: {question}
The function must use the `wn` model, store the final answer in a variable called `result`, and return it.
Only output valid Python code. Do not include markdown or explanations.
"""
try:
response = llm(prompt)[0]["generated_text"]
code_start = response.find("def answer")
if code_start != -1:
code = response[code_start:]
st.subheader("🧠 Generated Code")
st.code(code, language="python")
local_vars = {"wn": wn}
try:
exec(code, local_vars)
result = local_vars["answer"]()
st.subheader("📤 Output")
st.success(result)
except Exception as e:
st.subheader("📤 Output")
st.error(f"Error executing function: {e}")
else:
st.error("Could not extract Python function from LLM response.")
except Exception as e:
st.error(f"Error querying DeepSeek model: {e}")
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