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import torch
import torch.nn as nn
from transformers import T5Tokenizer, T5EncoderModel
import gradio as gr
import re
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
# === NLTK Downloads ===
nltk.download('punkt_tab')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('omw-1.4')
# === Preprocessing Function ===
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
def preprocess_text(text):
text = re.sub(r'[^A-Za-z\s]', '', text)
text = re.sub(r'\s+', ' ', text).strip()
text = text.lower()
tokens = word_tokenize(text)
tokens = [word for word in tokens if word not in stop_words]
tokens = [lemmatizer.lemmatize(word) for word in tokens]
return ' '.join(tokens)
# === Model Definition ===
class T5Classifier(nn.Module):
def __init__(self, model_name='t5-small', num_labels=2):
super(T5Classifier, self).__init__()
self.encoder = T5EncoderModel.from_pretrained(model_name)
self.classifier = nn.Linear(self.encoder.config.d_model, num_labels)
def forward(self, input_ids, attention_mask):
encoder_output = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
cls_representation = encoder_output.last_hidden_state[:, 0, :]
logits = self.classifier(cls_representation)
return logits
# === Load Model & Tokenizer ===
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5Classifier(model_name='t5-small', num_labels=2)
model.load_state_dict(torch.load("best_model.pth", map_location=device))
model.to(device)
model.eval()
# === Prediction Function ===
label_map = {0: "Negative", 1: "Positive"}
def predict_sentiment(text):
cleaned = preprocess_text(text)
inputs = tokenizer(cleaned, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs["attention_mask"].to(device)
with torch.no_grad():
logits = model(input_ids=input_ids, attention_mask=attention_mask)
pred = torch.argmax(logits, dim=1).item()
return label_map[pred]
# === Gradio Interface ===
demo = gr.Interface(
fn=predict_sentiment,
inputs=gr.Textbox(label="Enter Movie Review"),
outputs=gr.Text(label="Predicted Sentiment"),
title="🎬 T5 Movie Review Classifier",
description="Enter a movie review, and the model will predict whether the sentiment is Positive or Negative."
)
demo.launch()
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