trying to reverse changes

app.py

@@ -159,6 +159,132 @@ def relax_wrapper(structure_file, task_name, steps, fmax, charge, spin, relax_ce
 # ==== UI ====
 with gr.Blocks(theme=gr.themes.Ocean(), title="OrbMol Demo") as demo:
     with gr.Tabs():
+        # -------- HOME TAB --------
+        with gr.Tab("Home"):
+            with gr.Row():
+                # Columna izquierda con acordeones
+                with gr.Column(scale=1):
+                    gr.Markdown("## Learn more about OrbMol")
+                    
+                    with gr.Accordion("What is OrbMol?", open=False):
+                        gr.Markdown("""
+OrbMol is a suite of quantum-accurate machine learning models for molecular predictions. Built on the **Orb-v3 architecture**, OrbMol provides fast and accurate calculations of energies, forces, and molecular properties at the level of advanced quantum chemistry methods.
+
+The models combine the transferability of universal potentials with quantum-level accuracy, making them suitable for a wide range of applications in chemistry, materials science, and drug discovery.
+                        """)
+                    
+                    with gr.Accordion("Available Models", open=False):
+                        gr.Markdown("""
+**OMol** and **OMol-Direct**
+- **Training dataset**: OMol25 (>100M calculations on small molecules, biomolecules, metal complexes, and electrolytes)
+- **Level of theory**: ωB97M-V/def2-TZVPD with non-local dispersion; solvation treated explicitly
+- **Inputs**: total charge & spin multiplicity
+- **Applications**: biology, organic chemistry, protein folding, small-molecule drugs, organic liquids, homogeneous catalysis
+- **Caveats**: trained only on aperiodic systems → periodic/inorganic cases may not work well
+- **Difference**: OMol enforces energy–force consistency; OMol-Direct relaxes this for efficiency
+
+**OMat**
+- **Training dataset**: OMat24 (>100M inorganic calculations, from Materials Project, Alexandria, and far-from-equilibrium samples)
+- **Level of theory**: PBE/PBE+U with Materials Project settings; VASP 54 pseudopotentials; no dispersion
+- **Inputs**: No support for spin and charge. Spin polarization included but magnetic state cannot be selected
+- **Applications**: inorganic discovery, photovoltaics, alloys, superconductors, electronic/optical materials
+- **Caveats**: magnetic effects may be incompletely captured
+                        """)
+                    
+                    with gr.Accordion("Supported File Formats", open=False):
+                        gr.Markdown("""
+OrbMol supports the following molecular structure formats:
+- `.xyz` - XYZ coordinate files
+- `.pdb` - Protein Data Bank format
+- `.cif` - Crystallographic Information File
+- `.traj` - ASE trajectory format
+- `.mol` - MDL Molfile
+- `.sdf` - Structure Data File
+
+All formats are automatically converted internally for processing.
+                        """)
+                    
+                    with gr.Accordion("How to Use", open=False):
+                        gr.Markdown("""
+**Single Point Energy**: Upload a molecular structure and select a model to calculate energies and forces.
+
+**Molecular Dynamics**: Run time-dependent simulations to observe molecular behavior at different temperatures and conditions.
+
+**Relaxation/Optimization**: Find the minimum-energy configuration of your molecular structure.
+
+Each tab provides specific parameters you can adjust to customize your calculations.
+                        """)
+                    
+                    with gr.Accordion("Technical Foundation", open=False):
+                        gr.Markdown("""
+All models are based on the **Orb-v3 architecture**, the latest generation of Orb universal interatomic potentials. 
+
+Key features:
+- Graph neural network architecture
+- Equivariant message passing
+- Multi-task learning across different quantum chemistry methods
+- Billions of training examples across diverse chemical spaces
+- Sub-kcal/mol accuracy on test sets
+                        """)
+                    
+                    with gr.Accordion("Resources & Support", open=False):
+                        gr.Markdown("""
+- [Orb-v3 paper](https://arxiv.org/abs/2504.06231)
+- [Orb-Models GitHub repository](https://github.com/orbital-materials/orb-models)
+- For issues/questions, please open a GitHub issue or contact the developers
+
+**Citation**: If you use OrbMol in your research, please cite the Orb-v3 paper and the relevant dataset papers (OMol25/OMat24).
+                        """)
+                
+                # Columna derecha con contenido principal
+                with gr.Column(scale=2):
+                    gr.Image("logo_color_text.png",
+                             show_share_button=False,
+                             show_download_button=False,
+                             show_label=False,
+                             show_fullscreen_button=False)
+                    
+                    gr.Markdown("# OrbMol — Quantum-Accurate Molecular Predictions")
+                    
+                    gr.Markdown("""
+Welcome to the OrbMol demo! This interactive platform allows you to explore the capabilities of our quantum-accurate machine learning models for molecular simulations.
+
+## Quick Start
+
+Use the tabs above to access different functionalities:
+
+1. **Single Point Energy**: Calculate energies and forces for a given molecular structure
+2. **Molecular Dynamics**: Run MD simulations using OrbMol-trained potentials
+3. **Relaxation / Optimization**: Optimize molecular structures to their minimum-energy configurations
+
+Simply upload a molecular structure file in any supported format (`.xyz`, `.pdb`, `.cif`, `.traj`, `.mol`, `.sdf`) and select the appropriate model for your system.
+
+## Model Selection Guide
+
+**Choose OMol/OMol-Direct for:**
+- Organic molecules and biomolecules
+- Drug-like compounds
+- Metal-organic complexes
+- Molecules in solution
+- Systems where you need to specify charge and spin
+
+**Choose OMat for:**
+- Inorganic crystals and materials
+- Periodic systems
+- Bulk materials and alloys
+- Solid-state compounds
+
+Explore the accordions on the left to learn more about each model's capabilities, training data, and limitations.
+                    """)
+                    
+                    gr.Markdown("## Try an Example")
+                    gr.Markdown("""
+To get started quickly, navigate to any of the calculation tabs above and try one of these examples:
+- **Single Point Energy**: Upload a small molecule to see energy and force predictions
+- **Molecular Dynamics**: Run a short simulation at 300K to observe thermal motion
+- **Relaxation**: Optimize a distorted structure to find its equilibrium geometry
+                    """)
+
         # -------- SPE --------
         with gr.Tab("Single Point Energy"):
             with gr.Row():