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
import transformers
from transformers import BertModel, BertTokenizer
from PIL import Image
import requests
import io

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):
    #text = "I'm depressed"
    #model = model.to('cpu')
    tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
    encoding = tokenizer.encode_plus(text, max_length=32, add_special_tokens=True, # Add '[CLS]' and '[SEP]'
                                     return_token_type_ids=False,
                                     pad_to_max_length=True,
                                     return_attention_mask=True,
                                     return_tensors='pt')

    outputs = model(input_ids = encoding['input_ids'], attention_mask = encoding['attention_mask'])
    _, preds = torch.max(outputs, dim=1)
    face_url = "https://raw.githubusercontent.com/liangc40/ID2223_Sentimental_Analysis_Project/main/Image/"+ str(preds) + ".png"
    r = requests.get(face_url, stream=True)
    img = Image.open(io.BytesIO(r.content))
    #img = Image.open(requests.get(face_url, stream=True).raw)
    #print(preds)
    return img

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()