prediction.py

import streamlit as st
import torch
import torch.nn as nn
import numpy as np
import joblib
from transformers import AutoTokenizer, AutoModel
from rdkit import Chem
from rdkit.Chem import Descriptors, AllChem
from datetime import datetime
from db import get_database
import random

# ------------------------ Ensuring Deterministic Behavior ------------------------
random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# ------------------------ Load ChemBERTa Model + Tokenizer ------------------------
@st.cache_resource
def load_chemberta():
    tokenizer = AutoTokenizer.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
    model = AutoModel.from_pretrained("seyonec/ChemBERTa-zinc-base-v1")
    model.eval()
    model.to(device)
    return tokenizer, model

# ------------------------ Load Transformer Model ------------------------
class TransformerRegressor(nn.Module):
    def __init__(self, input_dim=2058, embedding_dim=768, ff_dim=1024, num_layers=2, output_dim=6):
        super().__init__()
        self.feat_proj = nn.Linear(input_dim, embedding_dim)
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=embedding_dim, nhead=8,
            dim_feedforward=ff_dim, dropout=0.0,
            batch_first=True
        )
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.regression_head = nn.Sequential(
            nn.Linear(embedding_dim, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, output_dim)
        )

    def forward(self, x):
        x = self.feat_proj(x)
        x = self.transformer_encoder(x)
        x = x.mean(dim=1)
        return self.regression_head(x)

@st.cache_resource
def load_model():
    model = TransformerRegressor()
    state_dict = torch.load("transformer_model(1).bin", map_location=device)
    model.load_state_dict(state_dict)
    model.eval()
    model.to(device)
    return model

# ✅ Load tokenizer/model globally
tokenizer, chemberta = load_chemberta()
model = load_model()

# ------------------------ Load Scalers ------------------------
scalers = {
    "Tensile Strength": joblib.load("scaler_Tensile_strength_Mpa_.joblib"),
    "Ionization Energy": joblib.load("scaler_Ionization_Energy_eV_.joblib"),
    "Electron Affinity": joblib.load("scaler_Electron_Affinity_eV_.joblib"),
    "logP": joblib.load("scaler_LogP.joblib"),
    "Refractive Index": joblib.load("scaler_Refractive_Index.joblib"),
    "Molecular Weight": joblib.load("scaler_Molecular_Weight_g_mol_.joblib")
}

# ------------------------ Descriptors ------------------------
def compute_descriptors(smiles: str):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        raise ValueError("Invalid SMILES string.")
    return np.array([
        Descriptors.MolWt(mol),
        Descriptors.MolLogP(mol),
        Descriptors.TPSA(mol),
        Descriptors.NumRotatableBonds(mol),
        Descriptors.NumHDonors(mol),
        Descriptors.NumHAcceptors(mol),
        Descriptors.FractionCSP3(mol),
        Descriptors.HeavyAtomCount(mol),
        Descriptors.RingCount(mol),
        Descriptors.MolMR(mol)
    ], dtype=np.float32)

# ------------------------ Fingerprints ------------------------
def get_morgan_fingerprint(smiles, radius=2, n_bits=1280):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        raise ValueError("Invalid SMILES string.")
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=n_bits)
    return np.array(fp, dtype=np.float32).reshape(1, -1)

# ------------------------ Embedding ------------------------
def get_chemberta_embedding(smiles: str):
    inputs = tokenizer(smiles, return_tensors="pt").to(device)
    with torch.no_grad():
        outputs = chemberta(**inputs)
    return outputs.last_hidden_state.mean(dim=1).cpu()

# ------------------------ Save to DB ------------------------
def save_to_db(smiles, predictions):
    doc = {
        "smiles": smiles,
        "predictions": {k: float(v) for k, v in predictions.items()},
        "timestamp": datetime.now()
    }
    db = get_database()
    db["polymer_predictions"].insert_one(doc)

# ------------------------ Streamlit UI ------------------------
def show():
    st.markdown("<h1 style='text-align: center; color: #4CAF50;'>🔬 Polymer Property Prediction</h1>", unsafe_allow_html=True)
    st.markdown("<hr style='border: 1px solid #ccc;'>", unsafe_allow_html=True)

    smiles_input = st.text_input("Enter SMILES Representation of Polymer")

    if st.button("Predict"):
        try:
            mol = Chem.MolFromSmiles(smiles_input)
            if mol is None:
                st.error("Invalid SMILES string.")
                return

            descriptors = compute_descriptors(smiles_input)
            descriptors_tensor = torch.tensor(descriptors, dtype=torch.float32).unsqueeze(0)

            fingerprint = get_morgan_fingerprint(smiles_input)
            fingerprint_tensor = torch.tensor(fingerprint, dtype=torch.float32)

            embedding = get_chemberta_embedding(smiles_input)

            combined_input = torch.cat([embedding, descriptors_tensor, fingerprint_tensor], dim=1)
            combined = combined_input.unsqueeze(1).to(device)

            with torch.no_grad():
                preds = model(combined).cpu().numpy()

            keys = list(scalers.keys())
            preds_rescaled = np.concatenate([
                scalers[key].inverse_transform(preds[:, [i]]) for i, key in enumerate(keys)
            ], axis=1)

            results = {key: round(val, 4) for key, val in zip(keys, preds_rescaled.flatten())}

            st.success("Predicted Properties:")
            for key, val in results.items():
                st.markdown(f"**{key}**: {val}")

            save_to_db(smiles_input, results)

        except Exception as e:
            st.error(f"Prediction failed: {e}")