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()