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| import streamlit as st | |
| from transformers import AutoModelForSequenceClassification | |
| import pandas as pd | |
| import numpy as np | |
| import torch | |
| from preprocess import Preprocess | |
| from utility import Utility | |
| st.title("Movie Genre Predictor") | |
| st.subheader("Enter the text you'd like to analyze.") | |
| text = st.text_input('Enter text') | |
| # out = model() | |
| model = AutoModelForSequenceClassification.from_pretrained("./checkpoint-36819") | |
| # model.to('cuda') | |
| if st.button("Predict"): | |
| st.write("Genre: ") | |
| preprocess = Preprocess() | |
| clean_plot = preprocess.apply(text) | |
| utility = Utility() | |
| id2label, label2id, tokenizer, tokenized_plot = utility.tokenize(clean_plot, ["Action","Drama", "Romance", "Comedy", "Thriller"]) | |
| st.write(tokenized_plot) | |
| input_ids = [np.asarray(tokenized_plot['input_ids'])] | |
| attention_mask = [np.asarray(tokenized_plot['attention_mask'])] | |
| st.write([np.asarray(tokenized_plot['input_ids'])]) | |
| st.write(clean_plot) | |
| # # create Pandas DataFrame | |
| # input_ids_labels_df = pd.DataFrame({'input_ids': xtrain_input_ids, 'attention_mask': xtrain_attention_mask, 'labels': ytrain.tolist()}) | |
| # # define data set object | |
| # TD = CustomTextDataset(torch.IntTensor(input_ids_labels_df['input_ids']), torch.IntTensor(input_ids_labels_df['attention_mask']),\ | |
| # torch.FloatTensor(input_ids_labels_df['labels'])) | |
| # input_ids_labels_val_df = pd.DataFrame({'input_ids': xval_input_ids, 'attention_mask': xval_attention_mask, 'labels': yval.tolist()}) | |
| # VD = CustomTextDataset(torch.IntTensor(input_ids_labels_val_df['input_ids']), torch.IntTensor(input_ids_labels_val_df['attention_mask']),\ | |
| # torch.FloatTensor(input_ids_labels_val_df['labels'])) | |
| # # trainer = Trainer( | |
| # # model, | |
| # # train_dataset=TD, | |
| # # eval_dataset=VD, | |
| # # tokenizer=tokenizer, | |
| # # compute_metrics=compute_metrics | |
| # # ) | |
| # # y_pred = trainer.predict(VD) | |
| # # y_pred = model(input_ids, attention_mask) | |
| # preds = torch.FloatTensor(y_pred[0]) | |
| # y_predictions = [] | |
| # predictions = [] | |
| # for pred in preds: | |
| # # apply sigmoid + threshold | |
| # sigmoid = torch.nn.Sigmoid() | |
| # probs = sigmoid(pred.squeeze().cpu()) | |
| # prediction = np.zeros(probs.shape) | |
| # prediction[np.where(probs >= 0.5)] = 1 | |
| # predictions.append(prediction) | |
| # y_pred = predictions | |
| # st.write(out) |