app.py

import gradio as gr
import torch
from minicons import cwe
from huggingface_hub import hf_hub_download
import os

from model import FFNModule, FeatureNormPredictor, FFNParams, TrainingParams


def predict (Word, Sentence, modelname):
    models = {'Bert Layer 8 to Binder': ('bert-base-uncased', 'bert8_to_binder'),
              'Albert Layer 8 to Binder': ('albert-xxlarge-v2', 'albert8_to_binder_opt_stop')}
    if Word not in Sentence: return "invalid input: word not in sentence"
    model_name = models[modelname][1]
    lm = cwe.CWE(models[modelname][0])

    model_path = hf_hub_download("jwalanthi/semantic-feature-classifiers", model_name+".ckpt", use_auth_token=os.environ['TOKEN'])
    label_path = hf_hub_download("jwalanthi/semantic-feature-classifiers", model_name+".txt", use_auth_token=os.environ['TOKEN'])

    model = FeatureNormPredictor.load_from_checkpoint(
        checkpoint_path=model_path,
        map_location=None
    )
    model.eval()

    with open (label_path, "r") as file:
        labels = [line.rstrip() for line in file.readlines()]

    data = (Sentence, Word)
    emb = lm.extract_representation(data, layer=8)
    pred = torch.nn.functional.relu(model(emb))
    pred = pred.squeeze(0)
    pred_list = pred.detach().numpy().tolist()
    
    output = [labels[i]+'\t'+str(pred_list[i]) for i in range(len(labels)) if pred_list[i] > 0.0]
    return "All Positive Predicted Values:\n"+"\n".join(output)

demo = gr.Interface(
    fn=predict,
    inputs=[
        "text", 
        "text", 
        gr.Radio(["Bert Layer 8 to Binder", "Albert Layer 8 to Binder"])
    ],
    outputs=["text"],
)

demo.launch(share=True)

if __name__ == "__main__":
    demo.launch(share=True)