import gradio as gr
import os
import transformers
from transformers import AutoModel, AutoTokenizer
import numpy as np
import torch

model_names = ['python']

models = {}
model = AutoModel.from_pretrained(f'ZarahShibli/tmp_trainer',return_dict=False)

device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("device",device)
MAX_LEN = 200

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', return_tensors='pt')

target_cols = [ 'DevelopmentNotes', 'Expand','Parameters', 'Summary', 'Usage']


def predict(comment_sentence):
    print(comment_sentence)
    # Set the model in evaluation mode
    model.eval()
    
    fin_outputs=[] # create an empty list to store outputs
    
    # Tokenize the comment sentence using the BERT tokenizer and encode it with special tokens
    inputs = tokenizer.encode_plus(
        comment_sentence,
        truncation=True,
        add_special_tokens=True,
        max_length=MAX_LEN,
        padding='max_length',
        return_token_type_ids=True,
        return_tensors='pt'
    )
    
    # Retrieve the input ids, attention mask, and token type ids from the encoded inputs
    ids = inputs['input_ids'].to(device, dtype=torch.long)
    #mask = inputs['attention_mask'].to(device, dtype=torch.long)
    #token_type_ids = inputs['token_type_ids'].to(device, dtype=torch.long)
    
    # Forward pass through the model
    with torch.no_grad():
        outputs = model(ids)

    
        print(outputs)
        print(outputs[0])
        #outputs = torch.from_numpy(outputs).float()
        fin_outputs.extend(torch.sigmoid(outputs[0]).detach().numpy().tolist())
    print(fin_outputs[0])
    # Convert the outputs to boolean values based on the threshold
    outputs_boolean = np.array(fin_outputs[0]) >= 0.25
    
    # Get the indices where outputs are true
    true_indices = np.where(outputs_boolean)[1]
    
    # Map the indices to their corresponding categories
    predicted_categories = [target_cols[idx] for idx in true_indices]

    return predicted_categories
    
iface = gr.Interface(fn=predict, 
                     inputs="text",  #gr.inputs.Dropdown(model_names, label='class')], 
                     outputs="text",
                    )
iface.launch()