Initialize clean space with current models

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Classifier_model.py

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+import torch.nn as nn
+import torch
+
+class classifymodel(nn.Module):
+    def __init__(self, dim=1024,num_classes=2):
+        super(classifymodel, self).__init__()
+
+        self.norm = nn.LayerNorm(dim)
+        self.linear = nn.Linear(dim, num_classes)
+
+    def forward(self, x):
+        x = self.norm(x)
+        x = self.linear(x)
+        return x
+
+    def load_weights(self, path):
+        if path is not None:
+            model_dict = torch.load(path, map_location=torch.device('cpu'))
+            self.load_state_dict(model_dict)
+

IR_encoder.py

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+import torch
+import torch.nn as nn
+import numpy as np
+
+
+class MlpBlock(nn.Module):
+    def __init__(self, channels):
+        super(MlpBlock, self).__init__()
+
+        self.block = nn.Sequential(
+            nn.Linear(channels, channels),
+            nn.GELU(),
+            nn.Linear(channels, channels),
+        )
+
+    def forward(self, input):
+        return input + self.block(input)
+
+
+class CnnBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, padding):
+        super(CnnBlock, self).__init__()
+        self.layer = nn.Sequential(
+            nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding),
+            nn.BatchNorm1d(out_channels),
+            nn.GELU()
+        )
+
+    def forward(self, input):
+        return self.layer(input)
+
+
+def l2norm(X, dim, eps=1e-8):
+    """L2-normalize columns of X
+    """
+    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+    X = torch.div(X, norm)
+    return X
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        pe = torch.zeros(max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1), :]
+        return x
+
+
+class IRModel(nn.Module):
+    def __init__(self, output_channels=768, channels=32, dim=1024, no_txtnorm=False):
+        super(IRModel, self).__init__()
+        # CCTE
+        self.cnn_layers = nn.Sequential(
+            CnnBlock(1, channels, kernel_size=3, stride=1, padding=1),
+            CnnBlock(channels, channels * 2, kernel_size=3, stride=2, padding=1),
+            CnnBlock(channels * 2, channels * 4, kernel_size=3, stride=2, padding=1),
+            CnnBlock(channels * 4, channels * 8, kernel_size=3, stride=2, padding=1),
+            CnnBlock(channels * 8, channels * 16, kernel_size=3, stride=2, padding=1),
+        )
+        self.linear_2_768 = nn.Sequential(nn.Linear(512, 768), nn.GELU())
+        self.positional_encoding = PositionalEncoding(768)
+        self.transformer_encoder_layer = nn.TransformerEncoderLayer(d_model=768, nhead=8, batch_first=True)
+        self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers=1)
+        self.norm = nn.LayerNorm(768)
+
+        # ESA
+        self.linear = nn.Linear(output_channels, dim)
+        self.linear1 = nn.Linear(dim, dim)
+        self.dropout = nn.Dropout(0.1)
+        self.no_txtnorm = no_txtnorm
+        self.init_weights()
+
+    def forward(self, input):
+        x_ir = self.cnn_layers(input.unsqueeze(1))
+
+        x_ir = x_ir.transpose(1, 2)
+        x_ir = self.linear_2_768(x_ir)
+        x_ir = self.positional_encoding(x_ir)
+        x_ir = self.transformer_encoder(x_ir)
+        x_ir = self.norm(x_ir)
+
+        cap_emb = self.linear(x_ir)
+        cap_emb = self.dropout(cap_emb)
+        features_in = self.linear1(cap_emb)
+        features_k_softmax = nn.Softmax(dim=1)(features_in - torch.max(features_in, dim=1)[0].unsqueeze(1))
+        feature_cap = torch.sum(features_k_softmax * cap_emb, dim=1)
+
+        if not self.no_txtnorm:
+            feature_cap = l2norm(feature_cap, dim=-1)
+
+
+        return feature_cap
+
+    def load_weights(self, path):
+        if path is not None:
+            model_dict = torch.load(path, map_location=torch.device('cpu'))
+            self.load_state_dict(model_dict)
+
+    def init_weights(self):
+        """Xavier initialization for the fully connected layer
+        """
+        for m in self.children():
+            if isinstance(m, nn.Linear):
+                r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
+                m.weight.data.uniform_(-r, r)
+                m.bias.data.fill_(0)
+            elif isinstance(m, nn.BatchNorm1d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()

README.md

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+---
+title: CSU IR Web
+emoji: 🦀
+colorFrom: pink
+colorTo: red
+sdk: gradio
+sdk_version: 5.34.2
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

SMILES_encoder.py

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+import torch
+from torch import nn
+import numpy as np
+import os
+from transformers import RobertaModel
+from transformers import RobertaTokenizer
+from transformers import RobertaConfig
+
+
+def l2norm(X, dim, eps=1e-8):
+    """L2-normalize columns of X
+    """
+    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+    X = torch.div(X, norm)
+    return X
+
+class SmilesModel(nn.Module):
+    def __init__(self,
+                 roberta_model_path,
+                 roberta_tokenizer_path,
+                 smiles_maxlen=300,
+                 vocab_size=181,
+                 max_position_embeddings=505,
+                 num_attention_heads=12,
+                 num_hidden_layers=6,
+                 type_vocab_size=1,
+                 no_txtnorm=False,
+                 feature_dim=768,
+                 embed_size=1024,
+                 **kwargs
+                 ):
+        super(SmilesModel, self).__init__(**kwargs)
+        self.smiles_maxlen = smiles_maxlen
+        self.feature_dim = feature_dim
+        self.t_prime = nn.Parameter(torch.tensor(np.log(10.0)))
+        self.bias = nn.Parameter(torch.tensor(-10.0))
+        if roberta_tokenizer_path is not None:
+            self.smiles_tokenizer = RobertaTokenizer.from_pretrained(
+                roberta_tokenizer_path, max_len=self.smiles_maxlen)
+
+
+            new_tokens = ["@", "/", "\\"]
+
+
+            self.smiles_tokenizer.add_tokens(new_tokens)
+
+        if roberta_model_path is None or not os.path.exists(roberta_model_path):
+            self.smiles_config = RobertaConfig(
+                vocab_size=vocab_size,
+                max_position_embeddings=max_position_embeddings,
+                num_attention_heads=num_attention_heads,
+                num_hidden_layers=num_hidden_layers,
+                type_vocab_size=type_vocab_size,
+                hidden_size=self.feature_dim
+            )
+            self.smiles_model = RobertaModel(config=self.smiles_config)
+        else:
+            self.smiles_config = RobertaConfig.from_pretrained(
+                roberta_model_path)
+            self.smiles_model = RobertaModel.from_pretrained(
+                roberta_model_path)
+        self.smiles_model.resize_token_embeddings(len(self.smiles_tokenizer))
+        self.model = self.smiles_model
+
+        # ESA
+        self.linear = nn.Linear(768, embed_size)
+        self.linear1 = nn.Linear(embed_size, embed_size)
+        self.dropout = nn.Dropout(0.1)
+        self.no_txtnorm = no_txtnorm
+
+        self.init_weights()
+
+    def encode(self, input, lengths):
+        lengths = lengths -1
+        input_ids, attention_mask = input
+        hidden_states = self.model(input_ids, attention_mask)[0]
+        hidden_states = hidden_states[:, 1:, :]
+        cap_emb = self.linear(hidden_states)
+        cap_emb = self.dropout(cap_emb)
+        max_len = int(lengths.max())
+        mask = torch.arange(max_len).expand(lengths.size(0), max_len).to(lengths.device)
+        mask = (mask < lengths.long().unsqueeze(1)).unsqueeze(-1)
+        cap_emb = cap_emb[:, :int(lengths.max()), :]
+        features_in = self.linear1(cap_emb)
+        features_in = features_in.masked_fill(mask == 0,-10000)
+        features_k_softmax = nn.Softmax(dim=1)(features_in-torch.max(features_in,dim=1)[0].unsqueeze(1))
+        attn = features_k_softmax.masked_fill(mask == 0,0)
+        feature_cap = torch.sum(attn * cap_emb,dim=1)
+
+        if not self.no_txtnorm:
+            feature_cap = l2norm(feature_cap, dim=-1)
+
+        return feature_cap
+
+    def init_weights(self):
+        """Xavier initialization for the fully connected layer
+        """
+        for m in self.children():
+            if isinstance(m, nn.Linear):
+                r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
+                m.weight.data.uniform_(-r, r)
+                m.bias.data.fill_(0)
+            elif isinstance(m, nn.BatchNorm1d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+    def load_weights(self, path):
+        if path is not None:
+            model_dict = torch.load(path, map_location=torch.device('cpu'))
+            self.load_state_dict(model_dict)
+
+    def get_parameter_number(self):
+        total_num = sum(p.numel() for p in self.parameters())
+        trainable_num = sum(p.numel() for p in self.parameters() if p.requires_grad)
+        return 'Total parameters: {}, Trainable parameters: {}'.format(total_num, trainable_num)
+

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import pandas as pd
+import jcamp
+from rdkit import Chem
+from rdkit.Chem import Draw
+import os
+import gzip
+from sklearn.calibration import calibration_curve
+from huggingface_hub import hf_hub_download
+from rdkit.Chem.Draw import rdMolDraw2D
+
+DEVICE = torch.device('cpu')
+TOKENIZER_PATH = r"tokenizer-smiles-roberta-1e_new"
+EXAMPLE_DIR = "example_files"
+CALIB_DATA_DIR = r"confidence_file"
+RETRIEVAL_WEIGHTS_DIR = r"model_weights"
+PROCESSED_DATA_DIR = r"processed_data"
+
+ADMIN_KEY = os.environ.get("ADMIN_KEY", "123456") 
+HF_TOKEN = os.environ.get("HF_TOKEN")
+PRIVATE_DATASET_REPO = "Hsqcsu/Derivative_PS_data" 
+
+def generate_mol_image(smiles, filename, width=600, height=600, bond_lw=3):
+    mol = Chem.MolFromSmiles(smiles)
+    if not mol: return None  
+    try:
+        mol = rdMolDraw2D.PrepareMolForDrawing(mol, kekulize=True)
+        drawer = rdMolDraw2D.MolDraw2DCairo(width, height)
+        opts = drawer.drawOptions()
+        opts.bondLineWidth = bond_lw  
+        opts.minFontSize = 15  
+        opts.maxFontSize = 45 
+        opts.padding = 0.15  
+        opts.useBWAtomPalette() 
+        opts.clearBackground = False
+        drawer.DrawMolecule(mol)
+        drawer.FinishDrawing()
+        with open(filename, 'wb') as f:
+            f.write(drawer.GetDrawingText())
+        return mol  
+    except Exception:
+        return None
+
+def generate_mol_grid_custom(res_smiles, legends, filename, mols_per_row=5, sub_img_size=(400, 400), bond_lw=3):
+    n_mols = len(res_smiles)
+    rows = (n_mols + mols_per_row - 1) // mols_per_row
+    full_width = mols_per_row * sub_img_size[0]
+    full_height = rows * sub_img_size[1]
+    
+    drawer = rdMolDraw2D.MolDraw2DCairo(full_width, full_height, sub_img_size[0], sub_img_size[1])
+    opts = drawer.drawOptions()
+    opts.bondLineWidth = bond_lw
+    opts.minFontSize = 15
+    opts.maxFontSize = 45
+    opts.legendFontSize = 40
+    opts.padding = 0.05
+    opts.useBWAtomPalette()
+    opts.clearBackground = True
+    
+    mols = []
+    for s in res_smiles:
+        m = Chem.MolFromSmiles(s)
+        if m:
+            m = rdMolDraw2D.PrepareMolForDrawing(m, kekulize=True)
+            mols.append(m)
+        else:
+            mols.append(Chem.MolFromSmiles("")) 
+            
+    drawer.DrawMolecules(mols, legends=legends)
+    drawer.FinishDrawing()
+    with open(filename, 'wb') as f:
+        f.write(drawer.GetDrawingText())
+    return filename
+
+def download_private_file(filename):
+    local_path = os.path.join(PROCESSED_DATA_DIR, filename)
+    if not os.path.exists(local_path):
+        try:   
+            path = hf_hub_download(
+                repo_id=PRIVATE_DATASET_REPO,
+                filename=filename,
+                repo_type="dataset", 
+                token=HF_TOKEN,
+                local_dir=PROCESSED_DATA_DIR
+            )
+            return path
+        except Exception as e:
+            print(f"Download failed {filename}: {e}")
+            return local_path
+    return local_path
+    
+def load_confidence_mappings(path):
+    mappings = {}
+    print("Loading Calibration Data...")
+    for i in range(1, 11):
+        file_path = os.path.join(path, f'top{i}_calib_data.pt')
+        if os.path.exists(file_path):
+            data = torch.load(file_path, weights_only=False)
+            prob_true, prob_pred = calibration_curve(data['flags'], data['scores'], n_bins=10, strategy='quantile')
+            mappings[i] = (prob_pred, prob_true)
+        else:
+            print(f"Warning: Recall@{i} mapping missing.")
+    return mappings
+
+
+CONFIDENCE_MAPPINGS = load_confidence_mappings(CALIB_DATA_DIR)
+
+
+def calculate_confidence(score, recall_k):
+    if recall_k not in CONFIDENCE_MAPPINGS:
+        return 0.0
+    prob_pred, prob_true = CONFIDENCE_MAPPINGS[recall_k]
+    min_score, min_conf = prob_pred[0], prob_true[0]
+    if score < min_score:
+        conf = score * (min_conf / min_score) if min_score > 0 else 0.0
+    else:
+        conf = np.interp(score, prob_pred, prob_true)
+    return max(0.0, float(conf))
+
+from IR_encoder import IRModel
+from SMILES_encoder import SmilesModel
+from Classifier_model import classifymodel
+from ir_process import preprocess_jdx_spectra_higer_500, preprocess_jdx_spectra_lower_500,preprocess_csv_spectra_higer_500,preprocess_csv_spectra_lower_than_500
+
+
+def load_model_with_hf_weights(model_instance, model_weights_file_path, device):
+    model_instance.load_state_dict(torch.load(model_weights_file_path, weights_only=False,map_location=device))
+    model_instance.to(device)
+    model_instance.eval()
+    return model_instance
+
+
+def load_gz_features(filename, device):
+    path = download_private_file(filename)
+    with gzip.open(path, 'rb') as f: 
+        features = torch.load(f, weights_only=False)
+    return features.to(device)
+
+
+def load_smiles_list(filename):
+    path = download_private_file(filename)
+    with open(path, 'r', encoding='utf-8') as f:
+        return [line.strip() for line in f if line.strip()]
+
+
+print("Initializing Models and Libraries...")
+
+
+ir_model_ret = load_model_with_hf_weights(IRModel(),
+                                          os.path.join(RETRIEVAL_WEIGHTS_DIR, "best_ir_model_0.9230379746835443.pth"),
+                                          DEVICE)
+smiles_model_ret = load_model_with_hf_weights(
+    SmilesModel(roberta_model_path=None, roberta_tokenizer_path=TOKENIZER_PATH, smiles_maxlen=300,
+                max_position_embeddings=505, vocab_size=181, feature_dim=768),
+    os.path.join(RETRIEVAL_WEIGHTS_DIR, "best_smiles_model_0.9230379746835443.pth"), DEVICE)
+
+smiles_model_clf = load_model_with_hf_weights(
+    SmilesModel(roberta_model_path=None, roberta_tokenizer_path=TOKENIZER_PATH, smiles_maxlen=300,
+                max_position_embeddings=505, vocab_size=181, feature_dim=768),
+    os.path.join(RETRIEVAL_WEIGHTS_DIR, "best_smiles_model_0.9230379746835443.pth"), DEVICE)
+smiles_classifier_model = load_model_with_hf_weights(classifymodel(),
+    os.path.join(RETRIEVAL_WEIGHTS_DIR, "best_PS_SMILES_Classifier_model.pth"),
+                                                     DEVICE)
+
+library_nist_features = load_gz_features("library_NIST_smiles_features_fp16.gz", DEVICE)
+general_smiles_nist = load_smiles_list("NIST_smiles_can.txt")
+
+library_derivative_ps_features = load_gz_features("library_Derivative_PS_smiles_features_fp16.gz", DEVICE)
+ps_smiles_derivative = load_smiles_list("smiles_Derivative_PS.txt")
+
+library_existed_ps_features = load_gz_features("library_Existing_PS_smiles_features_fp16.gz", DEVICE)
+ps_smiles_existed = load_smiles_list("smiles_Existing_PS.txt")
+
+ALL_LIBS = {
+    "NIST": (library_nist_features, general_smiles_nist),
+    "PS Existing Library": (library_existed_ps_features, ps_smiles_existed),
+    "PS Derivative Library": (library_derivative_ps_features, ps_smiles_derivative)
+}
+
+
+def process_custom_smiles(smiles_str):
+    smiles_list = [s.strip() for s in smiles_str.strip().split('\n') if s.strip()]
+    valid_smiles, original_map = [], {}
+    for s in smiles_list:
+        mol = Chem.MolFromSmiles(s)
+        if mol:
+            can = Chem.MolToSmiles(mol, canonical=True)
+            valid_smiles.append(can)
+            original_map[can] = s
+    if not valid_smiles: return None, [], []
+    tokenizer = smiles_model_ret.smiles_tokenizer
+    encoded = [tokenizer(s, max_length=300, padding='max_length', truncation=True, return_tensors='pt') for
+               s in valid_smiles]
+    ids = torch.cat([item['input_ids'] for item in encoded], dim=0).to(DEVICE)
+    mask = torch.cat([item['attention_mask'] for item in encoded], dim=0).to(DEVICE)
+    with torch.no_grad():
+        feats = smiles_model_ret.encode((ids, mask), mask.sum(dim=1)).cpu().to(torch.float16)
+    return feats, valid_smiles, [original_map[s] for s in valid_smiles]
+
+
+def run_unified_retrieval(ir_file, spectrum_type, library_name, custom_smiles_str, access_key,progress=gr.Progress()):
+    if library_name == "PS Derivative Library":
+        if access_key != ADMIN_KEY:
+            raise gr.Error("⛔ Access Denied: Incorrect Access Key for PS Derivative Library.")
+    # ----------------------
+
+    if ir_file is None: raise gr.Error("Please upload an IR spectrum file.")
+    if ir_file is None: raise gr.Error("Please upload an IR spectrum file.")
+    file_path = ir_file.name
+    ir_data = None
+
+    if file_path.lower().endswith('.csv'):
+        try:
+            df = pd.read_csv(file_path, header=None)
+            w, i = df.iloc[:, 0].values, df.iloc[:, 1].values
+            if spectrum_type == "Transmittance":
+                ir_data = preprocess_csv_spectra_higer_500(w, i) if w[0] > 500 else preprocess_csv_spectra_lower_than_500(w, i)
+        except Exception as e:
+            raise gr.Error(f"Error processing CSV: {str(e)}")
+
+    elif file_path.lower().endswith('.jdx'):
+        try:
+            data = jcamp.jcamp_readfile(file_path)
+            w, i = np.array(data['x']), np.array(data['y'])
+            if spectrum_type == "Absorbance":
+               ir_data = preprocess_jdx_spectra_higer_500(w, i) if w[0] > 500 else preprocess_jdx_spectra_lower_500( w, i)
+        except Exception as e:
+            raise gr.Error(f"Error processing JDX: {str(e)}")
+
+    if ir_data is None:
+        raise gr.Error("Unsupported file format or invalid selection. Please check your file and 'Type' setting.")
+        
+    ir_tensor = torch.tensor(ir_data, dtype=torch.float32).unsqueeze(0).to(DEVICE)
+    with torch.no_grad():
+        ir_feat = ir_model_ret(ir_tensor).to(torch.float16)
+
+    if library_name == "Custom Library":
+        lib_features, lib_smiles, display_smiles = process_custom_smiles(custom_smiles_str)
+        if lib_features is None: raise gr.Error("No valid Custom SMILES provided.")
+    else:
+        lib_features, lib_smiles = ALL_LIBS[library_name]
+        display_smiles = lib_smiles
+
+    sims = ir_feat.to(torch.float32) @ lib_features.to(DEVICE).to(torch.float32).t()
+    scores, indices = sims.topk(min(10, lib_features.shape[0]), dim=1)
+    res_smiles = [display_smiles[idx.item()] for idx in indices[0]]
+    res_scores = [sco.item() for sco in scores[0]]
+
+    recall1_score = res_scores[0]
+    best_conf = -1.0
+    best_k = 1
+
+    for k in range(1, 11):
+        current_conf = calculate_confidence(recall1_score, k)
+        if current_conf > best_conf:
+            best_conf = current_conf
+            best_k = k
+
+
+    conf_summary = (
+                f'<div style="background-color: #ffffff; color: #1a1a1a; padding: 20px; border-radius: 12px; border: 1px solid #ddd; text-align: center;">'
+                f'<h3 style="color: #00bfa5; margin-top: 0; margin-bottom: 10px;">📊 Confidence Analysis</h3>'
+                f'<p style="font-size: 0.9em; color: #666; text-align: justify; line-height: 1.5; margin: 10px 0; padding: 0 10px;">'
+                f'Please note that high confidence scores may occur even if the target substance is absent from the database, '
+                f'provided that structural analogs are present.'
+                f'Our method has proven that the top-ranked candidates have a high similarity to the correct substance, reaching an average Recall@1'
+                f'of 95.42% for the detection of 83 functional groups. This is also of reference value.'
+                f'</p>'
+                f'<hr style="border: 0; border-top: 1px solid #eee; margin: 15px 0;">'
+                f'<p style="font-size: 1.2em; margin: 10px 0;">There is a '
+                f'<span style="color:#008c7a; font-weight:bold; font-size: 1.3em;">{best_conf * 100:.2f}%</span> '
+                f'probability that the correct molecule is within '
+                f'<span style="font-weight:bold; color:#333;">Recall@1-{best_k}</span>.</p>'
+                f'<p style="font-size:0.9em; color:#666;">(Based on Similarity Score: {recall1_score:.4f})</p>'
+                f'</div>'
+    )
+
+    legends = [f"Recall@{i + 1}\nScore: {s:.4f}" for i, s in enumerate(res_scores)]
+    grid_filename = "search_results_grid.png"
+    grid_img_path = generate_mol_grid_custom(res_smiles, legends, grid_filename)
+
+    return (gr.update(visible=True),
+            grid_img_path, 
+            pd.DataFrame({"Recall@K": range(1, len(res_smiles) + 1), "SMILES": res_smiles, "Score": [round(s, 4) for s in res_scores]}),
+            gr.update(visible=True, value=pd.DataFrame({'wavenumber': w, 'intensity': i})),
+            gr.update(value=conf_summary, visible=True))
+
+
+def intelligent_smiles_prediction(text, file):
+    all_raw = []
+    if file:
+        with open(file.name, 'r', encoding='utf-8') as f: all_raw.extend([l.strip() for l in f if l.strip()])
+    if text: all_raw.extend([l.strip() for l in text.split('\n') if l.strip()])
+    valid_can, original_map, invalid = [], {}, []
+    for s in list(dict.fromkeys(all_raw)):
+        mol = Chem.MolFromSmiles(s)
+        if mol:
+            can = Chem.MolToSmiles(mol, canonical=True)
+            valid_can.append(can)
+            original_map[can] = s
+        else:
+            invalid.append(s)
+
+    results = []
+    if valid_can:
+        tokenizer = smiles_model_clf.smiles_tokenizer
+        for i in range(0, len(valid_can), 128):
+            batch = valid_can[i:i + 128]
+            encoded = [
+                tokenizer(s, max_length=300, padding='max_length', truncation=True, return_tensors='pt') for
+                s in batch]
+            ids = torch.cat([it['input_ids'] for it in encoded], dim=0).to(DEVICE)
+            mask = torch.cat([it['attention_mask'] for it in encoded], dim=0).to(DEVICE)
+            with torch.no_grad():
+                feats = smiles_model_clf.encode((ids, mask), mask.sum(dim=1))
+                preds = torch.argmax(smiles_classifier_model(feats), dim=1).cpu().numpy()
+            for s_can, p in zip(batch, preds):
+                results.append([original_map[s_can], "Psychoactive" if p == 1 else "Non-Psychoactive"])
+
+    return gr.update(value=pd.DataFrame(results, columns=["SMILES", "Prediction"]), visible=True), gr.update(
+        value=f"Invalid: {', '.join(invalid)}", visible=bool(invalid))
+
+
+CSS = """
+* { font-family: 'Times New Roman', Times, serif !important; }
+
+body { background-color: #ffffff !important; color: #333333 !important; }
+#header h1 { color: #00bfa5; text-align: center; margin-bottom: 20px; }
+
+
+.gradio-button { background: #00bfa5 !important; color: white !important; }
+
+
+.white-box { background-color: #f9f9f9 !important; border-radius: 12px; border: 1px solid #eee; padding: 20px; }
+
+
+.format-note { 
+    background-color: #f0fdfa; 
+    border: 1px solid #00bfa5; 
+    color: #065f46; 
+    padding: 15px; 
+    border-radius: 8px; 
+    text-align: center; 
+    margin-bottom: 20px; 
+    box-shadow: 0 2px 4px rgba(0,0,0,0.05); 
+}
+
+
+#white-text-example .gradio-label { color: #333333 !important; }
+#white-text-example button { color: #333333 !important; }
+"""
+
+
+with gr.Blocks(theme=gr.themes.Default(), css=CSS) as demo:
+    demo.load(None, None, None, js="""
+    () => {
+        document.body.classList.remove('dark');
+        document.querySelector('html').classList.remove('dark');
+    }
+    """)
+
+    gr.HTML('<div id="header"><h1>CSU-IR</h1></div>')
+    gr.HTML(
+        '<div class="format-note">⚠️ <b>Notice:</b> Currently only <b>.jdx</b> and <b>.csv</b> file formats are supported. '
+        'Users can download example files from the <a href="https://huggingface.co/Hsqcsu/CSU-IR/tree/main" target="_blank" style="color: #00bfa5; text-decoration: underline;">Hugging Face website</a> '
+        'and prepare raw data according to corresponding standards.</div>')
+
+
+    with gr.Tabs():
+        with gr.TabItem("Spectrum Retrieval"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    ir_input = gr.File(label="Upload Spectrum")
+                    gr.Markdown(
+                        "**Example usage guide:**\n- `2-Butene`: Transmittance. Recommended for **NIST** library.\n- `Ethyl tert-Butyl Ether`: Absorbance. Recommended for **NIST** library.\n- `4-fluoro-ABUTINACA`: Absorbance. Recommended for **PS** libraries.")
+                    gr.Examples(examples=[[os.path.join(EXAMPLE_DIR, "2-Butene.csv")],
+                                          [os.path.join(EXAMPLE_DIR, "Ethyl tert-Butyl Ether.jdx")],
+                                          [os.path.join(EXAMPLE_DIR, "4-fluoro-ABUTINACA.jdx")]],
+                                inputs=[ir_input], label="Click to use IR examples", elem_id="white-text-example")
+                    spec_type = gr.Radio(["Absorbance", "Transmittance"], label="Type", value="Absorbance")
+                    lib_choice = gr.Radio(
+                        choices=["NIST", "PS Existing Library", "PS Derivative Library", "Custom Library"],
+                        label="Library", value="NIST")
+                    access_key_input = gr.Textbox(
+                        label="🔑 Derivative Library Access Key", 
+                        placeholder="Required only for Derivative Library",
+                        type="password"
+                    )
+                    with gr.Group(visible=False) as custom_box:
+                        custom_smiles = gr.Textbox(lines=5,
+                                                   label="Custom SMILES List(One SMILES per line. Copy-paste Support)")
+                    lib_choice.change(lambda c: gr.update(visible=(c == "Custom Library")), inputs=[lib_choice],
+                                      outputs=[custom_box])
+                    run_btn = gr.Button("Run Retrieval", variant="primary")
+                with gr.Column(scale=2):
+                    plot_out = gr.LinePlot(x="wavenumber", y="intensity", x_lim=[4000, 500], visible=False)
+                    conf_display = gr.HTML(visible=False, elem_classes="white-box")
+                    with gr.Group(visible=False) as res_group:
+                        img_out = gr.Image(label="Structures")
+                        df_out = gr.DataFrame()
+
+            run_btn.click(
+                fn=run_unified_retrieval, 
+                inputs=[ir_input, spec_type, lib_choice, custom_smiles, access_key_input],
+                outputs=[res_group, img_out, df_out, plot_out, conf_display]
+            )
+
+        with gr.TabItem("PS-SMILES Classifier"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    sm_text = gr.Textbox(lines=10, label="Input SMILES")
+                    sm_file = gr.File(label="Upload .txt", file_types=[".txt"])
+                    gr.Markdown("#### Example file for classification")
+                    gr.Examples(
+                        examples=[[os.path.join(EXAMPLE_DIR, "NPS_smiles.txt")]],
+                        inputs=[sm_file],
+                        label="Click to use example file (NPS SMILES list)",
+                        elem_id="white-text-example"
+                    )
+                    clf_btn = gr.Button("Classify", variant="primary")
+                with gr.Column(scale=2):
+                    clf_df = gr.DataFrame(visible=False)
+                    clf_msg = gr.Markdown(visible=False)
+            clf_btn.click(fn=intelligent_smiles_prediction, inputs=[sm_text, sm_file], outputs=[clf_df, clf_msg])
+         
+
+if __name__ == "__main__":
+    demo.launch()

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example_files/4-fluoro-ABUTINACA.jdx

@@ -0,0 +1,301 @@
+##TITLE=4-fluoro-ABUTINACA (Lot# 0588385-34)
+##JCAMP-DX=5.01  $$ Nicolet v. 1
+##DATATYPE=INFRARED SPECTRUM
+##ORIGIN=SWGDRUG 15 May 2024
+##OWNER=SWGDRUG
+##LONGDATE=2022/10/18
+##TIME=22:15:49
+##SPECTROMETER/DATA SYSTEM=Nicolet iS10
+##DATA PROCESSING=
+Retrieved from a Library
+Subtraction or Addition
+Multiplied by a scalar
+##COMMENTS=
+Number of sample scans: 32
+Collection length: 47.68 sec
+Sample gain: 0.0
+Number of background scans: 32
+Background gain: 4.0
+Sample spacing:  0.000
+Number of scan points: 16672
+Resolution points: 8192
+Number of FFT points: 65536
+Apodization: N-B strong;
+Phase correction: Mertz;
+Laser frequency: 15798.67 cm-1
+Number of rejected sample scans: -1
+Number of rejected background scans: -1
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+##END=

example_files/Ethyl tert-Butyl Ether.jdx

@@ -0,0 +1,1215 @@
+##TITLE=Ethyl tert-Butyl Ether
+##JCAMP-DX=4.24
+##DATA TYPE=INFRARED SPECTRUM
+##CLASS=IUPAC A
+##ORIGIN=NIST, Anayltical Chemistry Division, 301-975-3108
+##OWNER=COPYRIGHT (C) 1998 by the U.S. Secretary of Commerce
+##DATE=98/10/1
+##$SPECTRA VERSION=1.00
+##$UNCERTAINTY IN Y=2.8 % relative (B=1.9E-04,C=3.0E-09,D=2.7E-14)
+##CAS NAME=tert-Butyl Ethyl Ether
+##CAS REGISTRY NO=637-92-3
+##MOLFORM=( C H3)3 C O C2 H5
+##MP=-94 C
+##BP=73.1 C
+##$NIST SOURCE=QUANT-IR
+##SPECTROMETER/DATA SYSTEM=IFS66V (Bruker)
+##INSTRUMENT PARAMETERS=KBr Beam splitter; MCT Detector; Multipass cell
+##STATE=gas
+##SAMPLE DESCRIPTION=ETBE Primary Gas Standard
+##PRESSURE=101.3 kPa
+##TEMPERATURE=23 C
+##SAMPLING PROCEDURE=1 L/min Flow
+##RESOLUTION=0.9640
+##DATA PROCESSING=3-Term B-H Apodization
+##XUNITS=cm-1
+##YUNITS=(micromol/mol)-1m-1 (base 10)
+##XFACTOR=1.0
+##YFACTOR=9.0949E-13
+##DELTAX=0.4820
+##FIRSTX=575.169
+##LASTX=3975.077
+##FIRSTY=-1.18E-07
+##MAXX=3974.59
+##MINX=575.17
+##MAXY=.0013573
+##MINY=-5.85E-07
+##NPOINTS=7053
+##XYDATA=(X++(Y..Y))
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+##END=

example_files/NPS_smiles.txt

@@ -0,0 +1,9 @@
+CCNCCn1c(Cc2ccc(OCC)cc2)nc2cc([N+](=O)[O-])ccc21
+CCNC1(c2cccs2)CCCCC1=O
+CCC(=O)N(c1ccccc1)C1CCN(C(=O)OC(C)(C)C)CC1
+CCCOC(=O)[C@H](c1ccccc1)[C@H]1CCCCN1
+CCC(=O)N(c1ccc(Br)cc1)C1CCN(CCc2ccccc2)CC1
+Cc1ccccc1N(C(=O)c1ccco1)C1CCN(CCc2ccccc2)CC1
+CCOc1ccc(Cc2nc3cc([N+](=O)[O-])ccc3n2CCN2CCCC2)cc1
+C[C@@H]1N=C(c2ccccc2Cl)c2cc([N+](=O)[O-])ccc2NC1=O
+ccc

ir_process.py

@@ -0,0 +1,132 @@
+import numpy as np
+from scipy.interpolate import interp1d
+import warnings
+
+def exp_func(x, a, b, c):
+    return a * np.exp(-b * x) + c
+
+# define ir process functions
+def preprocess_jdx_spectra_higer_500(wavenumbers, transmittances, method='cubic'):
+    wavenumbers = np.array(wavenumbers, dtype=float)
+    transmittances = np.array(transmittances, dtype=float)
+    valid_indices = np.where(transmittances != 0)[0]
+    wavenumbers = wavenumbers[valid_indices]
+    transmittances = transmittances[valid_indices]
+    num_points = int(4000 - wavenumbers[0])
+    target_wavenumbers = np.linspace(wavenumbers[0], wavenumbers[-1], num_points)
+    interpolator = interp1d(wavenumbers, transmittances, kind=method)
+    interpolated_transmittances = interpolator(target_wavenumbers)
+    baseline_value = find_baseline(transmittances)
+    num_zeros = max(int((wavenumbers[0] - 0.000000001) - 500) + 1, 0)
+    y_points = [interpolated_transmittances[0], baseline_value]
+    x_new = np.linspace(0, num_zeros - 1, num_zeros)
+    a = y_points[0] - y_points[1]
+    b = 0.03
+    c = y_points[1]
+    y_fit = exp_func(x_new, a, b, c)
+    y_fit_flipped = np.flip(y_fit)
+    padded_transmittances = np.concatenate((y_fit_flipped, interpolated_transmittances))
+    padded_transmittances = (padded_transmittances - np.min(padded_transmittances)) / (
+            np.max(padded_transmittances) - np.min(padded_transmittances))
+    if np.any(np.isnan(padded_transmittances)):
+        print("NaN values found in normalized absorbances")
+    if len(padded_transmittances) != 3500:
+        raise ValueError("The number of filled data points is not equal to 3500")
+    return padded_transmittances
+
+def preprocess_jdx_spectra_lower_500(wavenumbers, transmittances, method='cubic'):
+    wavenumbers = np.array(wavenumbers, dtype=float)
+    transmittances = np.array(transmittances, dtype=float)
+    valid_indices = np.where(transmittances != 0)[0]
+    wavenumbers = wavenumbers[valid_indices]
+    transmittances = transmittances[valid_indices]
+    closest_index = np.argmin(np.abs(wavenumbers - 500))
+    wavenumbers = wavenumbers[closest_index:]
+    transmittances = transmittances[closest_index:]
+    num_points = 3500
+    target_wavenumbers = np.linspace(wavenumbers[0], wavenumbers[-1], num_points)
+    interpolator = interp1d(wavenumbers, transmittances, kind=method)
+    interpolated_transmittances = interpolator(target_wavenumbers)
+    padded_transmittances = (interpolated_transmittances - np.min(interpolated_transmittances)) / (
+            np.max(interpolated_transmittances) - np.min(interpolated_transmittances))
+    if np.any(np.isnan(padded_transmittances)):
+        print("NaN values found in normalized absorbances")
+    if len(padded_transmittances) != 3500:
+        raise ValueError("The number of filled data points is not equal to 3500")
+    return padded_transmittances
+
+
+def preprocess_csv_spectra_higer_500(wavenumbers, transmittances, method='cubic'):
+    wavenumbers = np.array(wavenumbers, dtype=float)
+    transmittances = np.array(transmittances, dtype=float)
+    valid_indices = np.where(transmittances != 0)[0]
+    wavenumbers = wavenumbers[valid_indices]
+    transmittances = transmittances[valid_indices]
+    transmittances = transmittances / 100.0
+
+    num_points = int(4000 - wavenumbers[0])
+    target_wavenumbers = np.linspace(wavenumbers[0], wavenumbers[-1], num_points)
+    interpolator = interp1d(wavenumbers, transmittances, kind=method)
+    interpolated_transmittances = interpolator(target_wavenumbers)
+
+    try:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            absorbances = -np.log10(interpolated_transmittances)
+    except Exception as e:
+        print(f"Error in absorbance conversion: {e}")
+        return None, None
+
+    baseline_value = find_baseline(absorbances)
+    num_zeros = max(int((wavenumbers[0] - 0.000000001) - 500) + 1, 0)
+    y_points = [absorbances[0], baseline_value]
+    x_new = np.linspace(0, num_zeros - 1, num_zeros)
+    a = y_points[0] - y_points[1]
+    b = 0.03
+    c = y_points[1]
+    y_fit = exp_func(x_new, a, b, c)
+    y_fit_flipped = np.flip(y_fit)
+    padded_absorbances = np.concatenate((y_fit_flipped, absorbances))
+    padded_absorbances = (padded_absorbances - np.min(padded_absorbances)) / (
+            np.max(padded_absorbances) - np.min(padded_absorbances))
+    if np.any(np.isnan(padded_absorbances)):
+        print("NaN values found in normalized absorbances")
+    if len(padded_absorbances) != 3500:
+        raise ValueError("The number of filled data points is not equal to 3500")
+    return padded_absorbances
+
+
+def preprocess_csv_spectra_lower_than_500(wavenumbers, transmittances, method='cubic'):
+    wavenumbers = np.array(wavenumbers, dtype=float)
+    transmittances = np.array(transmittances, dtype=float)
+    valid_indices = np.where(transmittances != 0)[0]
+    wavenumbers = wavenumbers[valid_indices]
+    transmittances = transmittances[valid_indices]
+    transmittances = transmittances / 100.0
+    closest_index = np.argmin(np.abs(wavenumbers - 500))
+    wavenumbers = wavenumbers[closest_index:]
+    transmittances = transmittances[closest_index:]
+    target_wavenumbers = np.linspace(wavenumbers[0], wavenumbers[-1], 3500)
+    interpolator = interp1d(wavenumbers, transmittances, kind=method)
+    interpolated_transmittances = interpolator(target_wavenumbers)
+    try:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            absorbances = -np.log10(interpolated_transmittances)
+            if len(w) > 0 and issubclass(w[-1].category, RuntimeWarning):
+                raise RuntimeWarning("invalid value encountered in log10")
+        normalized_absorbances = (absorbances - np.min(absorbances)) / (np.max(absorbances) - np.min(absorbances))
+    except Exception as e:
+        print(f"Error in absorbance conversion or normalization: {e}")
+        return None, None
+    if np.any(np.isnan(normalized_absorbances)):
+        print("NaN values found in normalized absorbances")
+        return None, None
+    return normalized_absorbances
+
+
+
+def find_baseline(spectrum, window_size=50):
+    local_minima = [np.min(spectrum[i:i + window_size]) for i in range(0, len(spectrum), window_size)]
+    baseline_value = np.median(local_minima)
+    return baseline_value

model_weights/overall_best_nps_recall10_ir.pth

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processed_data/library_Existing_PS_smiles_features_fp16.gz

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processed_data/library_NIST_smiles_features_fp16.gz

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+CCCc1nn(C)c2c(=S)nc(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)[nH]c12
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+COc1ccc(C=O)cc1OC
+CCN(CC)CCOc1ccc(/C(=C(\Cl)c2ccccc2)c2ccccc2)cc1.O=C(O)CC(O)(CC(=O)O)C(=O)O
+CC1COC(c2ccc3c(c2)OCO3)O1
+CC1COC(c2ccccc2)CN1
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+CC1COC(c2ccccc2)C(C)N1
+CC12CC3CC(C)(C1)CC(N)(C3)C2
+COc1cc(CCNCc2ccccc2F)c(OC)cc1Cl
+CCN(CC)CCc1c[nH]c2cccc(OC(C)=O)c12.O=C(O)/C=C/C(=O)O
+COc1cc(CCNCc2ccccc2F)c(OC)cc1I
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+CCN(C)[C@H](C)[C@@H](O)c1ccccc1
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+CCCC(NC(C)C)C(=O)c1ccccc1
+CCCc1nn(C)c2c(=O)[nH]c(-c3cc(C(=O)CN4CCN(CC)CC4)ccc3OCC)nc12
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+CCOCCNC1(c2ccccc2)CCCCC1
+O=C(Nc1ccccc1)c1cn(CCCCCF)c2ccccc12
+COc1ccccc1CNCCc1cc(OC)c(C)c(C)c1OC
+CC(C)CC(=O)C1C(=O)c2ccccc2C1=O
+CC(C)(C)C(=O)OC1N=C(c2ccccc2)c2cc(Cl)ccc2NC1=O
+CCC(=O)N(c1ccccc1)C1(C(=O)OC)CCN(CCc2ccccc2)CC1
+CCN(C)CCc1c[nH]c2cccc(OC(C)=O)c12.O=C(O)/C=C/C(=O)O
+CN1C(=O)CN=C(c2ccc(Cl)cc2)c2cc(Cl)ccc21
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+c1ccc2c(c1)CCC2
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+NCC(CC(=O)O)c1ccccc1
+Cl.c1c2c(cc3c1OCO3)CNCC2
+CCC(C(=O)c1ccc2c(c1)CCCC2)N1CCCC1
+CCCCc1cc(OC)c(CC(C)N)cc1OC
+CCCCCc1cc(OC)c(CC(C)N)cc1OC
+COc1cc(C(C)C)c(OC)cc1CCN
+COc1ccc2c(CCN(C(C)C)C(C)C)c[nH]c2c1
+CCCCCn1cc(C(=O)c2cccc3ccccc23)cc1-c1ccc(F)cc1
+CCCCCn1cc(C(=O)c2cccc3ccccc23)cc1-c1ccccc1Cl
+Cc1oc2nc3c(c(NC(=O)CN4CCCC4=O)c2c1C)CCCC3
+COC(=O)[C@H]1[C@@H](c2ccc(Cl)c(Cl)c2)C[C@@H]2CC[C@H]1N2C.Cl
+COC(=O)[C@H]1[C@@H](c2ccc(Cl)c(Cl)c2)C[C@@H]2CC[C@H]1N2
+CNC(Cc1ccc2c(c1)OCO2)C1CC1
+CC(C)NC(C)C(=O)c1ccc2c(c1)OCO2
+COc1cc2c(cc1CC(C)N)OCO2
+CCC(=O)N(c1ccccc1)C1CCN(CCc2ccccc2)CC1F
+CNC(C)C(=O)c1cc(OC)c2c(c1)OCO2
+CN(C)C1Cc2cccc3[nH]cc(c23)C1
+c1ccc(CN2CCC(Nc3ccccc3)CC2)cc1
+CC(C)[C@H](NC(=O)c1cn(Cc2ccccc2)c2ccccc12)C(N)=O
+CC(C)(C)C(NC(=O)c1cn(Cc2ccccc2)c2ccccc12)C(N)=O
+CC(C)(C)[C@H](NC(=O)c1nn(Cc2ccccc2)c2ccccc12)C(N)=O
+COC(=O)[C@@H](NC(=O)c1cn(Cc2ccc(F)cc2)c2ccccc12)C(C)(C)C
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+CNC1(c2ccccc2F)CCCCC1=O
+CC(C)(C)NCC(O)c1cc(Cl)c(N)c(Cl)c1
+COc1ccccc1CN(C)C(C)Cc1ccc(C)cc1
+CCNC(C)C(=O)c1cccc(Cl)c1
+CNCCc1ccc2c(c1)OCO2
+Cc1ccc(C(=O)c2cn(CC3CCCCC3)c3ccccc23)c2ccccc12
+CCNC(C)C(=O)c1ccccc1Cl.Cl
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+CC(C)(C)[C@@H](NC(=O)c1cn(Cc2ccc(F)cc2)c2ccccc12)C(N)=O
+CCC(=O)N(c1ccccc1)C1CCN(CCc2ccco2)CC1
+CC1Nc2ccc(Cl)cc2S(=O)(=O)N1
+NC1Cc2ccc3c(c2C1)OCO3
+c1ccc(CC(Cc2ccccc2)N2CCCC2)cc1
+CNC(C)C(=O)c1ccc2c(c1)CCC2
+CN(C)[C@H]1CCCC[C@@H]1NC(=O)c1ccc(Br)cc1
+CCC(=O)N(c1ccccc1)C1(C(=O)OC)CCN(C)CC1
+O=C1Nc2ccc(Br)cc2C(c2ccccc2F)=NC1O
+CCCCn1c(C)c(C(=O)c2cccc3ccccc23)c2ccccc21
+CCCCCn1c(C)c(C(=O)c2ccc(OC)c3ccccc23)c2ccccc21
+CCCCCn1cc(C(=O)c2cccc3ccccc23)cc1-c1cccc2ccccc12
+S=C1CN=C(c2ccccc2)c2cc(Cl)ccc2N1
+CC(C)[C@H](NC(=O)c1cn(Cc2ccc(F)cc2)c2ncccc12)C(N)=O
+C=C(C)C(=O)N(c1ccccc1)C1CCN(CCc2ccccc2)CC1
+CC(NCc1ccccc1)C(=O)c1ccc2c(c1)OCO2.Cl
+CCNC(C)Cc1ccc(F)cc1
+CN(C)CCc1ccc(O)cc1
+CC(Cc1ccc2c(c1)OCCO2)N(C)O
+COC(=O)[C@@H](NC(=O)c1cn(CCCCCF)c2ncccc12)C(C)(C)C
+CN1C(=O)CN=C(c2ccccc2F)c2cc(N)ccc21
+Cc1ccc(C(=O)C(C)N2CCCCC2)cc1
+CCN(CC)CCNC(=O)c1ccc(N)cc1
+CN(C)[C@H]1CCCC[C@@H]1N(C)C(=O)c1ccc(Br)cc1
+CCCNC(C)C(=O)Nc1ccccc1C
+CN(C)Cc1c[nH]c2ccccc12
+O=C(C1CC1)N(c1ccccc1)C1CCNCC1
+CNC(C)C(=O)c1cccc(OC)c1
+CN[C@@H]1CCCC[C@H]1N(C)C(=O)c1ccc(Cl)c(Cl)c1
+COC(=O)C(Cc1ccccc1)NC(=O)c1cn(CCCCCF)c2ccccc12
+CCCCn1cc(C(=O)c2ccc(OC)cc2)c2ccccc21
+c1cc2cc[nH]c2cn1
+c1cnc2[nH]ccc2c1
+c1cnc2cc[nH]c2c1
+c1cc2[nH]ccc2cn1
+CCNC1(c2cccc(O)c2)CCCCC1
+COc1ccc(C(=O)N2CCCC2=O)cc1
+COc1ccc(C(=O)c2c[nH]c3ccccc23)cc1
+CCCN(C(=O)c1ccc(Cl)c(Cl)c1)[C@@H]1CCCC[C@H]1N(C)C
+CCC(=O)N(c1ccccc1)C(C)CCN(C)CCc1ccccc1
+CCNC1(c2ccc(OC)cc2)CCCCC1
+O=C(NCCN1CCOCC1)c1ccc(Cl)cc1
+O=C1CN=C(c2ccccc2F)c2cc(Cl)ccc2N1CC1CC1
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+CCN(CC)C(C)C(=O)c1ccc(C)cc1
+COC(=O)c1nn(Cc2ccc(F)cc2)c2ccccc12
+CN(C)[C@@H]1CCCC[C@H]1N(C)C(=O)Cc1ccc(-c2ccccc2)cc1
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+COC(=O)[C@@H](NC(=O)c1cn(CCCCCF)c2ccccc12)C(C)C
+CCCN(CCC)CCc1c[nH]c2cccc(OC(C)=O)c12
+Nc1ccc2c(c1)C(c1ccccc1Cl)=NCC(=O)N2
+CCCC(NC(=O)c1nn(Cc2ccc(F)cc2)c2ccccc12)C(=O)OC
+Fc1ccccc1C(Cc1ccccc1)N1CCN(C2CCCCC2)CC1
+Fc1ccc(C(Cc2ccccc2)N2CCN(C3CCCCC3)CC2)cc1
+Fc1cccc(C(Cc2ccccc2)N2CCN(C3CCCCC3)CC2)c1
+COc1ccc2[nH]cc(CCN3CCCC3)c2c1
+CC(C)CC(C)N
+Cc1ccc(C(=O)C(C)Br)cc1
+CC(C)[C@H](NC(=O)c1cc(-c2ccc(F)cc2)n(CCCCCF)n1)C(N)=O
+NC(=O)c1ncn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c1N
+Cc1sc[n+](CC(=O)c2ccccc2)c1C
+C=CC(=O)NCC(=O)O
+CC(=O)C(C#N)c1ccccc1
+COc1ccc(CNc2nc(N3CCC[C@H]3CO)ncc2C(=O)NCc2ncccn2)cc1Cl
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+NNC(=O)CN1CC(c2ccccc2)CC1=O
+CCOC(=O)C1(C)OC1c1ccc2c(c1)OCO2
+CN(C)C(=O)c1nc(CNC(=O)CN)n(-c2ccc(Cl)cc2C(=O)c2ccccc2Cl)n1
+CN(C)[C@@H]1C(=O)C(C(N)=O)=C(O)[C@@]2(O)C(=O)C3=C(O)c4c(O)cccc4[C@@](C)(O)[C@H]3C[C@@H]12
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+CCCCn1nc(C(=O)N[C@H](C(=O)OC)C(C)(C)C)c2ccccc21
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+CN(C)CCc1ccccc1
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+C/C(=C\c1ccccc1)C(=O)O

requirements.txt

@@ -0,0 +1,13 @@
+torch
+torchvision
+torchaudio
+gradio
+numpy
+pandas
+scipy
+jcamp
+rdkit
+transformers
+huggingface-hub
+tqdm
+scikit-learn

tokenizer-smiles-roberta-1e_new/merges.txt

@@ -0,0 +1,104 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
+H e
+L i
+B e
+N e
+N a
+M g
+A l
+S i
+C l
+A r
+C a
+G G
+T i
+C r
+M n
+F e
+C o
+N i
+C u
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tokenizer-smiles-roberta-1e_new/tokenizer_config.json

@@ -0,0 +1,3 @@
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+}

tokenizer-smiles-roberta-1e_new/vocab.json

@@ -0,0 +1,183 @@
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