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 rdkit.Chem import AllChem
from datetime import datetime
from db import get_database  # Ensure this module is available
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

# Check if CUDA is available for GPU acceleration
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)  # Send model to GPU if available
    return tokenizer, 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")
}

# ------------------------ 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,  # No dropout for consistency
            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():
    # Initialize the model architecture first
    model = TransformerRegressor()

    # Load the state_dict (weights) from the saved model file
    try:
        state_dict = torch.load("transformer_model(1).bin", map_location=device)  # Ensure loading on the correct device
        model.load_state_dict(state_dict)
        model.eval()
        model.to(device)  # Send model to GPU if available
    except Exception as e:
        raise ValueError(f"Failed to load model: {e}")

    return model

# ------------------------ Descriptors ------------------------
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)

# ------------------------ 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, tokenizer, chemberta):
    inputs = tokenizer(smiles, return_tensors="pt")
    with torch.no_grad():
        outputs = chemberta(**inputs)
    return outputs.last_hidden_state.mean(dim=1)  # Use average instead of CLS token

# ------------------------ Save to DB ------------------------
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)

# ------------------------ Streamlit App ------------------------
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:
            # Load the model
            model = load_model()

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

            # Load the ChemBERTa tokenizer and model
            tokenizer, chemberta = load_chemberta()

            # Compute Descriptors, Fingerprints, and Embedding
            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, tokenizer, chemberta)

            # Combine Inputs and Make Prediction
            combined_input = torch.cat([embedding, descriptors_tensor, fingerprint_tensor], dim=1)
            combined = combined_input.unsqueeze(1)

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

            preds_np = preds.numpy()
            keys = list(scalers.keys())

            # Rescale Predictions
            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())}

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

            # Save the results to the database
            save_to_db(smiles_input, results)

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