File size: 3,007 Bytes
099ef68 4279ecc 099ef68 4279ecc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | import streamlit as st
from pysd import read_vensim, read_xmile
import pandas as pd
import matplotlib.pyplot as plt
import tempfile
import os
import requests
st.set_page_config(page_title="System Dynamics Simulator with Insights", layout="wide")
st.title("📈 System Dynamics Simulator using PySD + HF Transformers")
st.markdown("Upload a **.mdl** or **.xmile** file, simulate it, and gain insights using Hugging Face LLMs.")
HF_API_TOKEN = st.secrets["HF_API_TOKEN"] if "HF_API_TOKEN" in st.secrets else st.text_input("Enter Hugging Face API Token", type="password")
HF_MODEL_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1"
def ask_llm(prompt):
if not HF_API_TOKEN:
return "🔒 API token required."
headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
payload = {"inputs": prompt}
response = requests.post(HF_MODEL_URL, headers=headers, json=payload)
if response.status_code == 200:
return response.json()[0]["generated_text"]
else:
return f"⚠️ Error: {response.text}"
uploaded_file = st.file_uploader("Choose a Vensim (.mdl) or XMILE (.xmile) file", type=["mdl", "xmile"])
if uploaded_file is not None:
suffix = os.path.splitext(uploaded_file.name)[-1].lower()
with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp_file:
tmp_file.write(uploaded_file.getvalue())
model_path = tmp_file.name
try:
if suffix == ".mdl":
model = read_vensim(model_path)
elif suffix == ".xmile":
model = read_xmile(model_path)
else:
st.error("Unsupported file format.")
st.stop()
st.success("✅ Model loaded successfully!")
if st.button("Run Simulation"):
result = model.run()
st.subheader("📊 Simulation Output")
st.dataframe(result)
st.subheader("📉 Plot Time-Series")
selected_vars = st.multiselect("Select variables to plot", result.columns.tolist(), default=result.columns.tolist())
if selected_vars:
for var in selected_vars:
fig, ax = plt.subplots()
ax.plot(result.index, result[var], label=var)
ax.set_xlabel("Time")
ax.set_ylabel(var)
ax.set_title(f"{var} over Time")
ax.grid(True)
st.pyplot(fig)
st.subheader("🧠 Ask a Question about the Model")
question = st.text_area("What would you like to know about the system dynamics model?")
if st.button("Get Insight from LLM"):
model_info = str(model.components)[:1000] # trim context
prompt = f"""The following is a system dynamics model fragment:
{model_info}
Question: {question}
"""
response = ask_llm(prompt)
st.markdown(f"**Answer:**\n\n{response}")
except Exception as e:
st.error(f"❌ Error: {str(e)}")
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