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app.py

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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')
+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()

best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8a008c909b7fdb52a57fc29896f7035ec95c40bd4826ff1ac87a42f19c672140
+size 141353634

requirements.txt

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+torch>=2.0.0
+transformers>=4.40.0
+nltk>=3.8.1
+gradio>=4.0.0