Adding running script

script.py

@@ -0,0 +1,115 @@
+import gradio as gr
+from predict_clickbait import generate_clickbait
+from datasets import load_dataset,DatasetDict,Dataset
+# from datasets import 
+from transformers import AutoTokenizer,AutoModelForSeq2SeqLM
+import numpy as np
+from sklearn.model_selection import train_test_split
+import pandas as pd
+from sklearn.utils.class_weight import compute_class_weight
+import torch
+import pandas as pd 
+from model import Model
+import imp
+import os
+import random
+import time
+import pickle
+import math
+from argparse import ArgumentParser
+from collections import namedtuple
+import mock
+
+from tqdm import tqdm
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from data import Dataset
+from util import save_checkpoint, ProgressMeter, AverageMeter, num_params
+from constants import *
+from predict_clickbait import generate_clickbait, tokenizer, classifier_tokenizer
+
+
+# imp.reload(model)
+pretrained_model = "./checkpoint-150/"
+generation_model = AutoModelForSeq2SeqLM.from_pretrained(pretrained_model, return_dict=True).to(device)
+
+device = 'cuda'
+pad_id = 0
+
+generation_model.eval()
+
+model_args = mock.Mock()
+model_args.task = 'clickbait'
+model_args.device = device
+model_args.checkpoint = './checkpoint-1464/'
+
+conditioning_model = Model(model_args, pad_id, len(dataset_info.index2word)) # no need to get the glove embeddings when reloading since they're saved in model ckpt anyway
+conditioning_model = conditioning_model.to(device)
+conditioning_model.eval()
+
+condition_lambda = 5.0
+length_cutoff = 50
+precondition_topk = 200
+
+
+conditioning_model.classifier
+
+model_args.checkpoint
+
+classifier_tokenizer = AutoTokenizer.from_pretrained(model_args.checkpoint, load_best_model_at_end=True)
+
+
+def rate_title(input_text, model, tokenizer, device='cuda'):
+  # input_text = {
+  #                 "postText": input_text['postText'],
+  #                 "truthClass" : input_text['truthClass']
+  #              }
+  tokenized_input = preprocess_function_title_only_classification(input_text,tokenizer=tokenizer)
+  # print(tokenized_input.items())
+  dict_tokenized_input = {k : torch.tensor([v]).to(device) for k,v in tokenized_input.items() if k != 'labels'}
+  predicted_class = float(model(**dict_tokenized_input).logits)
+  actual_class = input_text['truthClass']
+
+  # print(predicted_class, actual_class)
+  return {'predicted_class' : predicted_class}
+
+def preprocess_function_title_only_classification(examples,tokenizer=None):
+    model_inputs = tokenizer(examples['postText'], padding="longest", truncation=True, max_length=25)
+      
+    model_inputs['labels'] = examples['truthClass']
+
+    return model_inputs
+
+
+
+def clickbait_generator(article_content, condition_lambda=5.0):
+    # result = "Hi {}! 😎. The Mulitple of {} is {}".format(name, number, round(number**2, 2))
+    results = generate_clickbait(model=generation_model, 
+                        tokenizer=tokenizer, 
+                        conditioning_model=conditioning_model, 
+                        input_text=[None], 
+                        dataset_info=dataset_info, 
+                        precondition_topk=precondition_topk,
+                        length_cutoff=length_cutoff,
+                        condition_lambda=condition_lambda,
+                        article_content=article_content,
+                        device=device)
+    
+    return results[0].replace('</s>', '').replace('<pad>', '')
+
+title = "Clickbait generator"
+description = """
+"Use the [Fudge](https://github.com/yangkevin2/naacl-2021-fudge-controlled-generation) implementation fine tuned for our purposes to try and create news headline you are looking for!"
+"""
+
+article = "Check out [the codebase for our model](https://github.com/dsvilarkovic/naacl-2021-fudge-controlled-generation) that this demo is based off of."
+
+
+app = gr.Interface(
+    title = title,
+    description = description,
+    label = 'Article content or paragraph', 
+    fn = clickbait_generator, 
+    inputs=["text", gr.Slider(0, 100, step=0.1, value=5.0)], outputs="text")
+app.launch()