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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
# 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):
# Process the image and the prompt
with open('data/fasta/example.fasta', 'w') as f:
f.write('>{}\n'.format("protein_name"))
f.write('{}\n'.format(protein.strip()))
os.system("python esm_scripts/extract.py esm2_t36_3B_UR50D data/fasta/example.fasta data/emb_esm2_3b --repr_layers 36 --truncation_seq_length 1024 --include per_tok")
esm_emb = torch.load("data/emb_esm2_3b/protein_name.pt")['representations'][36]
esm_emb = F.pad(esm_emb.t(), (0, 1024 - len(esm_emb))).t().to('cuda')
samples = {'name': ['test_protein'],
'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
# 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="pil", label="Upload sequence"), gr.Textbox(label="Prompt", value="taxonomy prompt")],
outputs=gr.Textbox(label="Generated description"),
description=description
)
# Launch the interface
iface.launch()
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