app.py

import os
import torch
import torch.nn as nn
import pandas as pd
import torch.nn.functional as F
from lavis.models.protein_models.protein_function_opt import Blip2ProteinMistral
from lavis.models.base_model import FAPMConfig
import spaces
import gradio as gr
from esm_scripts.extract import run_demo
from esm import pretrained, FastaBatchedDataset

# from transformers import EsmTokenizer, EsmModel


# Load the model
model = Blip2ProteinMistral(config=FAPMConfig(), esm_size='3b')
model.load_checkpoint("model/checkpoint_mf2.pth")
model.to('cuda')


@spaces.GPU
def generate_caption(protein, prompt):

    esm_emb = torch.load('data/emb_esm2_3b/P18281.pt')['representations'][36]
    torch.save(esm_emb, 'data/emb_esm2_3b/example.pt')
    '''
    inputs = tokenizer([protein], return_tensors="pt", padding=True, truncation=True).to('cuda')
    with torch.no_grad():
        outputs = model_esm(**inputs)
    esm_emb = outputs.last_hidden_state.detach()[0]
    '''
    print("esm embedding generated")
    esm_emb = F.pad(esm_emb.t(), (0, 1024 - len(esm_emb))).t().to('cuda')
    print("esm embedding processed")
    samples = {'name': ['protein_name'],
               'image': torch.unsqueeze(esm_emb, dim=0),
               'text_input': ['none'],
               'prompt': [prompt]}

    # Generate the output
    prediction = model.generate(samples, length_penalty=0., num_beams=15, num_captions=10, temperature=1.,
                                repetition_penalty=1.0)

    return prediction
    # return "test"


# Define the FAPM interface
description = """Quick demonstration of the FAPM model for protein function prediction. Upload an protein sequence to generate a function description. Modify the Prompt to provide the taxonomy information.

The model used in this app is available at [Hugging Face Model Hub](https://huggingface.co/wenkai/FAPM) and the source code can be found on [GitHub](https://github.com/xiangwenkai/FAPM/tree/main)."""

iface = gr.Interface(
    fn=generate_caption,
    inputs=[gr.Textbox(type="text", label="Upload sequence"), gr.Textbox(type="text", label="Prompt")],
    outputs=gr.Textbox(label="Generated description"),
    description=description
)

# Launch the interface
iface.launch()