app.py

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()