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
from datetime import datetime
from db import get_database  # This must be available in your repo

# 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()
    return tokenizer, model

tokenizer, chemberta = load_chemberta()

# 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")
}

# Model Definition
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=0.1,
            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)

# Load model
@st.cache_resource
def load_model():
    model = TransformerRegressor()
    model.load_state_dict(torch.load("transformer_model.pt", map_location=torch.device("cpu")))
    model.eval()
    return model

model = load_model()

# Descriptor computation
def compute_descriptors(smiles: str):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        raise ValueError("Invalid SMILES string.")
    
    descriptors = [
        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)
    ]
    return np.array(descriptors, dtype=np.float32)

# Embedding function
def get_chemberta_embedding(smiles: str):
    inputs = tokenizer(smiles, return_tensors="pt")
    with torch.no_grad():
        outputs = chemberta(**inputs)
    return outputs.last_hidden_state[:, 0, :]  # CLS token

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

# Main Streamlit UI + prediction
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)

            embedding = get_chemberta_embedding(smiles_input)

            combined = torch.cat([embedding, descriptors_tensor], dim=1).unsqueeze(1)  # (1, 1, 2058)

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

            preds_np = preds.numpy()

            keys = list(scalers.keys())
            preds_rescaled = np.concatenate([
                scalers[keys[i]].inverse_transform(preds_np[:, [i]])
                for i in range(6)
            ], axis=1)

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

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

            # Save to MongoDB
            save_to_db(smiles_input, results)

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