app.py

import gradio as gr
from rdkit import Chem
from rdkit.Chem import Descriptors
import pandas as pd
import matplotlib.pyplot as plt
import io
import base64
import os

# دالة لحساب خصائص ADMET
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"])

# المعالجة الرئيسية
def analyze_smiles(file):
    try:
        # قراءة المحتوى باستخدام os
        file_path = file.name if hasattr(file, "name") else file
        with open(file_path, "r", encoding="utf-8") as f:
            content = f.read()

        smiles_list = [line.strip() for line in content.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)

        # Excel file
        excel_buffer = io.BytesIO()
        final_df.to_excel(excel_buffer, index=False, engine="openpyxl")
        excel_data = excel_buffer.getvalue()
        excel_b64 = base64.b64encode(excel_data).decode()

        # Plot image
        plot_df = final_df.dropna(subset=["LogP"])
        plt.figure(figsize=(10, 6))
        plt.barh(plot_df["SMILES"], plot_df["LogP"], color="skyblue")
        plt.xlabel("LogP (Lipophilicity)")
        plt.title("LogP per SMILES")
        plt.gca().invert_yaxis()
        plt.tight_layout()
        img_buffer = io.BytesIO()
        plt.savefig(img_buffer, format="png")
        img_buffer.seek(0)
        img_b64 = base64.b64encode(img_buffer.read()).decode()
        plt.close()

        # Markdown table
        table_md = final_df.to_markdown(index=False)

        return (
            "✅ التحليل ناجح!",
            table_md,
            f'<a download="ADMET_Analysis.xlsx" href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{excel_b64}">⬇️ تحميل ملف Excel</a>',
            f'<img src="data:image/png;base64,{img_b64}" width="600"/>'
        )

    except Exception as e:
        return f"❌ خطأ أثناء المعالجة: {str(e)}", "", "", ""

# واجهة Gradio
with gr.Blocks() as demo:
    gr.Markdown("## 🧪 تحليل ADMET من ملف SMILES (.txt)")

    with gr.Row():
        smiles_file = gr.File(label="📄 ارفع ملف .txt يحتوي على SMILES", file_types=[".txt"])
        run_btn = gr.Button("🚀 تحليل ADMET")

    status = gr.Textbox(label="📢 الحالة")
    table = gr.Textbox(label="📊 جدول النتائج (Markdown)", lines=12)
    download = gr.HTML()
    image = gr.HTML()

    run_btn.click(fn=analyze_smiles, inputs=[smiles_file], outputs=[status, table, download, image])

demo.launch()