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, LLM, Norm, Layer):
    models = {'BERT': 'bert-base-uncased',
              'ALBERT': 'albert-xxlarge-v2',
              'roBERTa': 'roberta-base'}
    if Word not in Sentence: return "invalid input: word not in sentence"
    model_name_hf = LLM.lower()
    norm_name_hf = Norm.lower()
    lm = cwe.CWE(models[LLM])

    repo_id = "jwalanthi/semantic-feature-classifiers"
    subfolder = "{model_name_hf}_models_all"
    name_hf = f"{model_name_hf}_to_{norm_name_hf}_layer{Layer}"

    model_path = hf_hub_download(repo_id = repo_id, subfolder=subfolder, filename=f"{name_hf}.ckpt", use_auth_token=os.environ['TOKEN'])
    label_path = hf_hub_download(repo_id = repo_id, subfolder=subfolder, filename=f"{name_hf}.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\t\t\t\t\t\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"]),
        gr.Radio(["Binder", "McRae", "Buchanan"]),
        gr.Slider(0,12, step=1)
    ],
    outputs=["text"],
)

demo.launch()

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