import os
import joblib
import numpy as np
import onnxruntime as rt
import sys
import requests
from io import BytesIO
import urllib.request
from morgan_desc import *
from physchem_desc import *
import tempfile

CHUNKSIZE = 1024

# root = os.path.abspath(os.path.dirname(__file__))
# sys.path.append(root)

class FragmentEmbedder(object):
    def __init__(self):
    
        url = 'https://huggingface.co/ligdis/fpred/resolve/main/morgan_descriptor.joblib' # The URL of the file you want to load
        with urllib.request.urlopen(url) as response:     # Download the file
            self.morgan_desc = joblib.load(BytesIO(response.read()))
    
        url = 'https://huggingface.co/ligdis/fpred/resolve/main/physchem_descriptor.joblib' # The URL of the file you want to load
        with urllib.request.urlopen(url) as response:     # Download the file
            self.physchem_desc = joblib.load(BytesIO(response.read()))

    def _chunker(self, l, n):
        for i in range(0, len(l), n):
            yield l[i : i + n]

    def encoder_inference(self, X):
#        sess = rt.InferenceSession(os.path.join(self.models_dir, "encoder_model.onnx"))  
        url = 'https://huggingface.co/ligdis/fpred/resolve/main/encoder_model.onnx'
        response = requests.get(url)
        
        if response.status_code == 200: # Ensure the request was successful
            with tempfile.NamedTemporaryFile(delete=False, suffix='.onnx') as temp_file:     # Create a temporary file to save the model
                temp_file.write(response.content)
                temp_file_path = temp_file.name
            sess = rt.InferenceSession(temp_file_path) # Load the model using InferenceSession   
#            model_bytes = BytesIO(response.content) # Create a file-like object from the byte data
#            sess = rt.InferenceSession(model_bytes)  
        else:
            st.write(f"Failed to fetch model from {url}. Status code: {response.status_code}")
    
        input_name = sess.get_inputs()[0].name
        output_name = sess.get_outputs()[0].name
        output_data = sess.run(
            [output_name], {input_name: np.array(X, dtype=np.float32)}
        )
        Y = np.array(output_data[0])
        return Y

    def transform(self, smiles):
        X = None
        for smiles_chunk in self._chunker(smiles, CHUNKSIZE):
            X_0 = self.morgan_desc.transform(smiles_chunk)
            X_1 = self.physchem_desc.transform(smiles_chunk)
            X_i = np.hstack([X_0, X_1])
            X_o = self.encoder_inference(X_i)
            if X is None:
                X = X_o
            else:
                X = np.vstack([X, X_o])
        return X