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import torch
import torch.nn as nn
from transformers import DistilBertTokenizer
import gradio as gr
import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
# Load preprocessing tools
nltk.download('stopwords')
nltk.download('punkt_tab')
nltk.download('wordnet')
stop_words = set(stopwords.words("english"))
lemmatizer = WordNetLemmatizer()
# Preprocessing function
def preprocess_text(text):
text = re.sub(r'[^A-Za-z\s]', '', text)
text = re.sub(r'https?://\S+|www\.\S+', '', text)
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)
# Define class mapping
label_dict = {
0: "sadness",
1: "joy",
2: "love",
3: "anger",
4: "fear",
5: "surprise"
}
# Load tokenizer
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
max_len = 32
# Define the GRU Classifier
class GRUClassifier(nn.Module):
def __init__(self, vocab_size, embed_dim, hidden_dim, num_classes):
super(GRUClassifier, self).__init__()
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
self.gru = nn.GRU(embed_dim, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim, num_classes)
def forward(self, input_ids):
x = self.embedding(input_ids)
out, _ = self.gru(x)
out = out[:, -1, :]
return self.fc(out)
# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GRUClassifier(vocab_size=tokenizer.vocab_size, embed_dim=128, hidden_dim=64, num_classes=6)
model.load_state_dict(torch.load("best_gru_model.pth", map_location=device))
model.to(device)
model.eval()
# Inference function
def classify_emotion(text):
cleaned = preprocess_text(text)
tokens = tokenizer(cleaned, truncation=True, padding='max_length', max_length=max_len, return_tensors='pt')
input_ids = tokens['input_ids'].to(device)
with torch.no_grad():
outputs = model(input_ids)
prediction = torch.argmax(outputs, dim=1).item()
return label_dict[prediction]
# Gradio Interface
iface = gr.Interface(fn=classify_emotion,
inputs=gr.Textbox(lines=2, placeholder="Enter a sentence..."),
outputs="text",
title="Emotion Classifier (GRU)",
description="Predicts emotion from text. Classes: sadness, joy, love, anger, fear, surprise")
if __name__ == "__main__":
iface.launch()
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