import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
#from src.utils import plogp, sf_decode, sim
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.Chem import Descriptors
import selfies as sf
from rdkit.Chem import RDConfig
import os
import sys
sys.path.append(os.path.join(RDConfig.RDContribDir, 'SA_Score'))
import sascorer

def get_largest_ring_size(mol):
    cycle_list = mol.GetRingInfo().AtomRings()
    if cycle_list:
        cycle_length = max([len(j) for j in cycle_list])
    else:
        cycle_length = 0
    return cycle_length

def plogp(smile):
    if smile:
        mol = Chem.MolFromSmiles(smile)
        if mol:
            log_p = Descriptors.MolLogP(mol)
            sas_score = sascorer.calculateScore(mol)
            largest_ring_size = get_largest_ring_size(mol)
            cycle_score = max(largest_ring_size - 6, 0)
            if log_p and sas_score and largest_ring_size:
                p_logp = log_p - sas_score - cycle_score
                return p_logp
            else: 
                return -100
        else:
            return -100
    else:
        return -100
    
def sf_decode(selfies):
    try:
        decode = sf.decoder(selfies)
        return decode
    except sf.DecoderError:
        return ''
    
def sim(input_smile, output_smile):
    if input_smile and output_smile:
        input_mol = Chem.MolFromSmiles(input_smile)
        output_mol = Chem.MolFromSmiles(output_smile)
        if input_mol and output_mol:
            input_fp = AllChem.GetMorganFingerprint(input_mol, 2)
            output_fp = AllChem.GetMorganFingerprint(output_mol, 2)
            sim = DataStructs.TanimotoSimilarity(input_fp, output_fp)
            return sim
        else: return None
    else: return None 


def greet(name):

    tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-large-opt")
    model = AutoModelForSeq2SeqLM.from_pretrained("zjunlp/MolGen-large-opt")
    
    input = name
    
    sf_input = tokenizer(input, return_tensors="pt")
    molecules = model.generate(
                    input_ids=sf_input["input_ids"],
                    attention_mask=sf_input["attention_mask"],
                    do_sample=True,
                    max_length=100,
                    min_length=5,
                    top_k=30,
                    top_p=1,
                    num_return_sequences=10
                    )
    sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
    sf_output = list(set(sf_output))
    input_sm = sf_decode(input)
    sm_output = [sf_decode(sf) for sf in sf_output]
    
    
    
    input_plogp = plogp(input_sm)
    plogp_improve = [plogp(i)-input_plogp for i in sm_output]
    
    
    simm = [sim(i,input_sm) for i in sm_output]
    
    candidate_selfies = {"candidates": sf_output, "improvement": plogp_improve, "sim": simm}
    data = pd.DataFrame(candidate_selfies)
    
    return data[(data['improvement']> 0) & (data['sim']>0.4)]

  
    



examples = [
            
    ['[C][C][=Branch1][C][=O][N][C][C][O][C][C][O][C][C][O][C][C][Ring1][N]'],['[C][C][S][C][C][S][C][C][C][S][C][C][S][C][Ring1][=C]']

]



iface = gr.Interface(fn=greet, inputs="text", outputs="numpy", title="Molecular Language Model as Multi-task Generator",examples=examples)
iface.launch()