Create app.py

app.py

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+# ✅ dependencies: gradio, rdkit-pypi, pandas, matplotlib, openpyxl
+import gradio as gr
+import pandas as pd
+from rdkit import Chem
+from rdkit.Chem import Descriptors
+import matplotlib.pyplot as plt
+import io
+import base64
+
+def get_admet(smiles):
+    mol = Chem.MolFromSmiles(smiles)
+    if not mol:
+        return pd.Series([None]*7, index=["MW", "LogP", "TPSA", "HBA", "HBD", "RotB", "Lipinski"])
+    mw = Descriptors.MolWt(mol)
+    logp = Descriptors.MolLogP(mol)
+    tpsa = Descriptors.TPSA(mol)
+    hba = Descriptors.NumHAcceptors(mol)
+    hbd = Descriptors.NumHDonors(mol)
+    rotb = Descriptors.NumRotatableBonds(mol)
+    lipinski = "✅" if (mw <= 500 and logp <= 5 and hba <= 10 and hbd <= 5) else "❌"
+    return pd.Series([mw, logp, tpsa, hba, hbd, rotb, lipinski],
+                     index=["MW", "LogP", "TPSA", "HBA", "HBD", "RotB", "Lipinski"])
+
+def analyze_admet(file):
+    smiles_list = [line.strip() for line in file.read().decode().splitlines() if line.strip()]
+    df = pd.DataFrame({"SMILES": smiles_list})
+    admet_df = df["SMILES"].apply(get_admet)
+    final_df = pd.concat([df, admet_df], axis=1)
+
+    # Save Excel to buffer
+    excel_buffer = io.BytesIO()
+    final_df.to_excel(excel_buffer, index=False)
+    excel_buffer.seek(0)
+    excel_data = base64.b64encode(excel_buffer.read()).decode()
+
+    # Create and save plot to buffer
+    plt.figure(figsize=(12, 6))
+    plot_df = final_df.dropna(subset=["LogP"])
+    plt.barh(plot_df["SMILES"], plot_df["LogP"], color='skyblue')
+    plt.xlabel("LogP")
+    plt.title("LogP Values per SMILES")
+    plt.gca().invert_yaxis()
+    plt.tight_layout()
+    img_buffer = io.BytesIO()
+    plt.savefig(img_buffer, format='png')
+    img_buffer.seek(0)
+
+    return (
+        final_df.to_markdown(index=False),
+        f"data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{excel_data}",
+        img_buffer
+    )
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧪 ADMET Analysis from SMILES File")
+    with gr.Row():
+        smiles_file = gr.File(label="📄 Upload SMILES .txt File")
+        run_btn = gr.Button("🚀 Analyze")
+    result = gr.Textbox(label="📊 Results Table (Markdown)", lines=20)
+    download_excel = gr.File(label="⬇️ Download Excel")
+    image_output = gr.Image(label="📈 LogP Plot")
+
+    run_btn.click(fn=analyze_admet, inputs=smiles_file, outputs=[result, download_excel, image_output])
+
+demo.launch()