import gradio as gr
import numpy as np
from transformers import pipeline
from model import DepressionClassifier
import hopsworks
import joblib
import torch
from huggingface_hub import hf_hub_download

class_names = ['Not Depressed', 'Depressed']
pt_file = hf_hub_download(repo_id="liangc40/sentimental_analysis", filename="model.pt")

model = DepressionClassifier(len(class_names), 'bert-base-cased')
model.load_state_dict(torch.load(pt_file, map_location=torch.device('cpu')))




model.eval()
#pipe = pipeline(model="liangc40/sentimental_analysis")

#project = hopsworks.login(project='liangc40')
#fs = project.get_feature_store()


#mr = project.get_model_registry()
#model = mr.get_model("sentimental_analysis_model", version=1)
#model_dir = model.download()
#model = joblib.load(model_dir + "/sentimental_analysis_model.pkl")

def analyse(text):
    label = model(text)
    return label

with gr.Blocks() as demo:
    gr.Markdown("<h1><center>Sentiment Analysis with Fine-tuned BERT Model")

    inputs_text=gr.Textbox(placeholder='Type your text for which you want know the sentiment', label='Text')
    text_button = gr.Button('Analyse Sentiment')
    output_text_sentiment = gr.Textbox(placeholder='Sentiment of the text.', label='Sentiment')
    text_button.click(analyse, inputs = inputs_text, outputs = output_text_sentiment)

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