#-------------------------------------------------libraries---------------------------------------------------------------------------------------------------------------
import torch
import numpy as np
import gradio as gr
import matplotlib.pyplot as plt
from transformers import AutoTokenizer, EsmModel
from sklearn.metrics.pairwise import cosine_similarity
from Bio.PDB import PDBParser, PDBIO
from Bio.PDB.StructureBuilder import StructureBuilder
import tempfile
import os

#----------------------------------------------------Analysis--------------------------------------------------------------------------------------------------------
# Load ESM-1b model and tokenizer
model = EsmModel.from_pretrained("facebook/esm1b_t33_650M_UR50S", output_hidden_states=True)
tokenizer = AutoTokenizer.from_pretrained("facebook/esm1b_t33_650M_UR50S")

def compute_residue_scores(seq):
    inputs = tokenizer(seq, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        embedding = outputs.last_hidden_state[0]  # shape (L+2, d)

    L = len(seq)
    embedding = embedding[1:L+1]  # shape (L, d)

    sim_matrix = cosine_similarity(embedding.detach().cpu().numpy())
    residue_scores = np.sum(sim_matrix, axis=1)
    norm_scores = 100 * (residue_scores - np.min(residue_scores)) / (np.max(residue_scores) - np.min(residue_scores))

    return norm_scores

def inject_bfactors_into_pdb(pdb_file, scores):
    parser = PDBParser(QUIET=True)
    structure = parser.get_structure("prot", pdb_file.name)

    i = 0
    for model in structure:
        for chain in model:
            for residue in chain:
                if i >= len(scores):
                    break
                for atom in residue:
                    atom.bfactor = float(scores[i])
                i += 1

    out_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdb").name
    io = PDBIO()
    io.set_structure(structure)
    io.save(out_path)

    return out_path

def process(seq, pdb_file):
    scores = compute_residue_scores(seq)
    pdb_with_scores = inject_bfactors_into_pdb(pdb_file, scores)
    return pdb_with_scores

# Gradio Interface
demo = gr.Interface(
    fn=process,
    inputs=[
        gr.Textbox(label="Input Protein Sequence (1-letter code)"),
        gr.File(label="Upload PDB File", file_types=[".pdb"])
    ],
    outputs=gr.File(label="Modified PDB with Scores in B-factor Column"),
    title="ESM-1b Residue Scoring: B-factor Injection for Structural Visualization"
)

demo.launch()