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text-classification-demo-2
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import numpy as np

# import gradio as gr


import os

import tempfile

import shutil

from trainer import Trainer

def predict(input_text, model_type):
    if model_type in ['lstm', 'bilstm']:
        predicted_label = trainer.predict(input_text )
    elif model_type == 'max_ent':
        predicted_label = trainer.predict_maxent(input_text)
    elif model_type == 'svm':
        predicted_label = trainer.predict_svm(input_text)
    
    return str(predicted_label)
    # pass
    
def predict_omni(input_text, model_type):
    predicted_label_net = trainer.predict(input_text )
    predicted_label_maxent = trainer_maxent.predict_maxent(input_text )
    predicted_label_svm = trainer_svm.predict_svm(input_text )
    # if model_type in ['lstm', 'bilstm']:
    #     predicted_label = trainer.predict(input_text )
    # elif model_type == 'max_ent':
    #     predicted_label = trainer.predict_maxent(input_text)
    # elif model_type == 'svm':
    #     predicted_label = trainer.predict_svm(input_text)
    predicted_text = f"LSTM: {predicted_label_net}, Max Ent: {predicted_label_maxent}, SVM: {predicted_label_svm}"
    return predicted_text
    # pass
    

def create_demo():

    USAGE = """## Text Classification
    
    """


    with gr.Blocks() as demo:

        gr.Markdown(USAGE)

        # demo = 
        # gr.Interface(
        #     predict,
        #     # gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
        #     gr.File(type="filepath"),
        #     gr.File(type="filepath"),
        #     cache_examples=False
        # )
        
        input_file = gr.File(type="filepath")
        output_file = gr.File(type="filepath")
        
        gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
        
        # gr.Interface(
        #     predict,
        #     # gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
        #     input_file,
        #     output_file,
        #     cache_examples=False
        # )
        
        # inputs = input_file
        # outputs = output_file
        # gr.Examples(
        #     examples=[os.path.join(os.path.dirname(__file__), "./gradio_inter/20231104_017.pkl")],
        #     inputs=inputs,
        #     fn=predict,
        #     outputs=outputs,
        # )
        

    return demo
    
if __name__ == "__main__":
    
    vocab_size = 8000
    sequence_len = 150
    
    # batch_size = 1024
    batch_size = 256
    nn_epochs = 20
    model_type = "lstm"
    
    # model_type = "bilstm"
    
    # model_type = "max_ent"
    
    # trainer = Trainer(vocab_size, sequence_len, batch_size, nn_epochs, model_type)
    # print(f"Trainer loaded")
    
    
    model_type = "lstm"
    
    trainer = Trainer(vocab_size, sequence_len, batch_size, nn_epochs, model_type)
    
    model_type = "max_ent"
    trainer_maxent = Trainer(vocab_size, sequence_len, batch_size, nn_epochs, model_type)
    
    model_type = "svm"
    trainer_svm = Trainer(vocab_size, sequence_len, batch_size, nn_epochs, model_type)
    
    
    while True:
        input_text = input()
        # if model_type in ["lstm", "bilstm"]:
        # label = predict(input_text, model_type)
        label = predict_omni(input_text, model_type)
        # elif model_type in ["max_ent"]:
        #     label = 
        print(label)
        
    # demo = create_demo()
    # demo.launch()
    # python app_local.py