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app.py

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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()

best_gru_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fde25b76a7949a8378b504f2e2458bfbdbbea7700608adb02899767d309436e9
+size 15780232

requirements.txt

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+torch
+transformers
+gradio
+nltk