import os
import pandas as pd
import rdkit
import molvs
import tqdm
import glob
from rdkit import Chem

standardizer = molvs.Standardizer()
fragment_remover = molvs.fragment.FragmentRemover()


# AID mapping {without : with detergent}
filter_map = {
    "585": "584",
    "1476": "1478",
    "485341": "485294"
}

# AID -> CID set maaping
cid_sets = {}

# Gather CIDs
for target_aid in filter_map.values():
    file_name = f"pubchem_aid_{target_aid}_active.csv"
    file_full_path = os.path.join("./active", file_name)

    if os.path.exists(file_full_path):
        df = pd.read_csv(file_full_path)
        cid_sets[target_aid] = set(df["CID"].tolist())
    else:
        print(f"Warning: file for AID {target_aid} not found!")

# Fliter
for target_aid, filter_aid in filter_map.items():
    file_name = f"pubchem_aid_{target_aid}_active.csv"
    file_full_path = os.path.join("./active", file_name)

    if not os.path.exists(file_full_path):
        print(f"Skipping {target_aid}, file not found.")
        continue

    df = pd.read_csv(file_full_path)

    before = len(df)
    df = df[~df["CID"].isin(cid_sets[filter_aid])]
    after = len(df)

    output_path = f"./active/pubchem_aid_{target_aid}_active_filtered.csv"
    df.to_csv(output_path, index=False)

    print(f"{target_aid}: removed {before - after} compounds from {before}, saved to {output_path}")


filtered_files = [
    "pubchem_aid_585_active_filtered.csv",
    "pubchem_aid_1476_active_filtered.csv",
    "pubchem_aid_485341_active_filtered.csv"
]


for file in filtered_files:
    file_path = os.path.join("./active", file)
    file_name = os.path.splitext(file)[0]

    # Read file
    active_df = pd.read_csv(file_path)
    smiles_series = active_df["CanonicalSMILES"]
    active_df["curated_SMILES"] = None
    cid = active_df["CID"]

    valid_indices = []
    invalid_smiles = []
    warning_smiles = []

    for idx, smiles in smiles_series.items():
        mol = Chem.MolFromSmiles(smiles)
        compound_cid = cid.iloc[idx]

        if mol is None:
            invalid_smiles.append({
                'CID': compound_cid,
                'SMILES': smiles,
                'Reason': "MolFromSmiles returned None"
            })
            continue

        results = molvs.validate_smiles(smiles)
        if len(results) > 0:
            warning_smiles.append({
                'CID': compound_cid,
                'SMILES': smiles,
                'Reason': results
            })
            continue

        mol = standardizer.standardize(mol)
        mol = fragment_remover.remove(mol)
        standardized = Chem.MolToSmiles(mol)

        active_df.at[idx, "curated_SMILES"] = standardized
        valid_indices.append(idx)

    # Save outputs
    valid_df = active_df.loc[valid_indices].reset_index(drop=True)
    valid_df = valid_df.drop(columns=["CanonicalSMILES"])
    valid_df = valid_df.rename(columns={"curated_SMILES": "SMILES"})
    aid = file_name.replace("pubchem_aid_", "").replace("_active_filtered", "")
    valid_df.insert(0, "AID", aid)

    invalid_df = pd.DataFrame(invalid_smiles)
    warning_df = pd.DataFrame(warning_smiles)
    invalid_df.insert(0, "AID", aid)
    warning_df.insert(0, "AID", aid)

    valid_df.to_csv(f'./curated/{file_name}_curated.csv', index=False)
    invalid_df.to_csv(f'./curated/{file_name}_invalid_smiles.csv', index=False)
    warning_df.to_csv(f'./curated/{file_name}_molvs_validation.csv', index=False)

    print(f"Finished curation for {file_name}")
    print(f"  Valid: {len(valid_df)}, Invalid: {len(invalid_df)}, Warnings: {len(warning_df)}\n")