Delete main.py

main.py

@@ -1,125 +0,0 @@
-from flask import Flask,render_template,url_for,request, session
-from werkzeug.utils import secure_filename
-import torch
-import pytorch_lightning as pl
-from transformers import T5ForConditionalGeneration,T5TokenizerFast as T5Tokenizer, AdamW
-from werkzeug.datastructures import  FileStorage
-
-
-MODEL_NAME ="t5-base"
-tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
-
-class NewsSummaryModel(pl.LightningModule):
-    def __init__(self):
-        super().__init__()
-        self.model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME, return_dict=True)
-
-    def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None):
-
-        output = self.model(
-            input_ids,
-            attention_mask = attention_mask,
-            labels = labels,
-            decoder_attention_mask = decoder_attention_mask
-        )
-        return output.loss, output.logits
-
-    def training_step(self, batch, batch_idx):
-        input_ids = batch["text_input_ids"]
-        attention_mask = batch["text_attention_mask"]
-        labels = batch["labels"]
-        labels_attention_mask = batch["labels_attention_mask"]
-
-        loss, outputs = self(
-            input_ids = input_ids,
-            attention_mask = attention_mask,
-            decoder_attention_mask = labels_attention_mask,
-            labels = labels
-        )
-
-        self.log("train_loss", loss, prog_bar =True, logger=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        input_ids = batch["text_input_ids"]
-        attention_mask = batch["text_attention_mask"]
-        labels = batch["labels"]
-        labels_attention_mask = batch["labels_attention_mask"]
-
-        loss, outputs = self(
-            input_ids = input_ids,
-            attention_mask = attention_mask,
-            decoder_attention_mask = labels_attention_mask,
-            labels = labels
-        )
-
-        self.log("val_loss", loss, prog_bar =True, logger=True)
-        return loss
-
-    def test_step(self, batch, batch_idx):
-        input_ids = batch["text_input_ids"]
-        attention_mask = batch["text_attention_mask"]
-        labels = batch["labels"]
-        labels_attention_mask = batch["labels_attention_mask"]
-
-        loss, outputs = self(
-            input_ids = input_ids,
-            attention_mask = attention_mask,
-            decoder_attention_mask = labels_attention_mask,
-            labels = labels
-        )
-
-        self.log("test_loss", loss, prog_bar =True, logger=True)
-        return loss
-
-    def configure_optimizers(self):
-        return AdamW(self.parameters(), lr=0.0001)
-
-
-filename = 'model.pth'
-model = torch.load(open(filename, 'rb'))
-
-def summarize(text):
-    text_encoding = tokenizer(
-        text,
-        max_length=512,
-        padding = "max_length",
-        truncation = True,
-        return_attention_mask = True,
-        add_special_tokens = True,
-        return_tensors = "pt"
-)
-    generated_ids = model.model.generate(
-        input_ids = text_encoding["input_ids"],
-        attention_mask = text_encoding["attention_mask"],
-        max_length = 150,
-        num_beams=2,
-        repetition_penalty = 2.5,
-        length_penalty=1.0,
-        early_stopping = True
-)
-
-    preds = [
-        tokenizer.decode(gen_id, skip_special_tokens = True, clean_up_tokenization_spaces=True)
-        for gen_id in generated_ids
-    ]
-
-    return "".join(preds)
-
-
-app = Flask(__name__)
-
-@app.route('/')
-def home():
-	return render_template('home.html')
-
-@app.route('/predict',methods=['POST'])
-def predict():
-    if request.method == 'POST':
-        message = request.form['message']
-        data = [message]
-        summary = summarize(data)
-    return render_template('result.html',Summary=summary)
-
-if __name__ == '__main__':
-	app.run()