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README.md

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+---
+license: apache-2.0
+tags:
+- materials-science
+- deep-learning
+- crystal-structure
+- chemistry
+---
+
+# 🔬 MatGraph CLI
+
+A modern CLI and GraphQL API tool for Material Science Deep Learning Pipelines. 
+
+**Powered by M3GNet Universal Potentials**
+
+This repository contains the Gradio Space logic and documentation for `matgraph-cli`. 
+
+## 🚀 Installation
+```bash
+pip install matgraph-cli
+```
+
+## 🛠 Features
+- Predict Thermodynamic Stability (M3GNet)
+- Structural Relaxation & Geometry Optimization (ASE)
+- Generative Discovery via Elemental Substitution
+- X-Ray Diffraction (XRD) Simulation
+- Phonon Density of States (DOS)
+- High-Performance GraphQL Server
+
+## 🔗 Links
+- [GitHub Repository](https://github.com/Himan-D/matgraph-cli)
+- [PyPI Package](https://pypi.org/project/matgraph-cli/)

app.py

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+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()

requirements.txt

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+matgraph-cli==1.5.1
+pandas