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e0789da | 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 | import gradio as gr
from matgraph.sdk import MatGraphSDK
import pandas as pd
def predict_material(formula, api_key):
if not api_key:
return "Please enter your Materials Project API Key."
try:
sdk = MatGraphSDK(api_key=api_key)
results = sdk.predict(formula=formula, model="m3gnet")
if not results:
return "No data found for this formula."
# Format results into a dataframe
df = pd.DataFrame([{
"ID": r["material_id"],
"Formula": r["formula"],
"Crystal System": r["crystal_system"],
"Predicted Energy (eV)": round(r["m3gnet_energy"], 3) if r.get("m3gnet_energy") else "N/A"
} for r in results])
return df
except Exception as e:
return f"Error: {str(e)}"
with gr.Blocks(title="MatGraph CLI: Deep Learning for Material Science", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🔬 MatGraph Explorer")
gr.Markdown("Predict thermodynamic stability and properties of materials using M3GNet Universal Potentials.")
with gr.Row():
with gr.Column():
formula_input = gr.Textbox(label="Chemical Formula (e.g., LiFePO4)", placeholder="LiFePO4")
api_input = gr.Textbox(label="Materials Project API Key", type="password")
btn = gr.Button("Predict Properties", variant="primary")
with gr.Column():
output_table = gr.Dataframe(label="Polymorph Predictions")
btn.click(fn=predict_material, inputs=[formula_input, api_input], outputs=[output_table])
gr.Markdown("Powered by [matgraph-cli](https://pypi.org/project/matgraph-cli/)")
demo.launch()
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