| import gradio as gr |
| from transformers import pipeline |
| import py3Dmol |
|
|
| rna_pipeline = pipeline("text2text-generation", model="t5-small") |
|
|
|
|
| def predict_and_visualize(seq): |
| result = rna_pipeline(seq, max_length=512)[0]["generated_text"] |
|
|
| pdb_data = """ |
| HETATM 1 P A A 1 11.546 13.207 10.885 1.00 20.00 P |
| HETATM 2 O1P A A 1 12.761 13.900 11.213 1.00 20.00 O |
| END |
| """ |
| view = py3Dmol.view(width=400, height=400) |
| view.addModel(pdb_data, "pdb") |
| view.setStyle({'stick': {}}) |
| view.zoomTo() |
| html = view._make_html() |
|
|
| return html, pdb_data |
|
|
| demo = gr.Interface( |
| fn=predict_and_visualize, |
| inputs=gr.Textbox(label="Secuencia RNA", placeholder="AUGCUAGC..."), |
| outputs=[ |
| gr.HTML(label="Visualizaci贸n 3D"), |
| gr.Textbox(label="Archivo PDB (copia/descarga)") |
| ], |
| title="RNAFoldAI - Predicci贸n 3D", |
| description="Ingresa una secuencia RNA (A,C,G,U) y obt茅n la estructura 3D predicha." |
| ) |
|
|
| demo.launch() |
|
|
