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app.py

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+import torch
+import gradio as gr
+import sys
+import os
+
+# Ensure the script can find the models and utils
+sys.path.append(os.path.abspath('.'))
+
+from models.transformer_imdb import TransformerClassifier
+
+# Constants from training logs
+VOCAB_SIZE = 100684
+MODEL_PATH = 'models/transformer_imdb.pth'
+DEVICE = torch.device('cpu')
+
+# Initialize and load the model
+def load_model():
+    model = TransformerClassifier(vocab_size=VOCAB_SIZE).to(DEVICE)
+    if os.path.exists(MODEL_PATH):
+        model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
+        model.eval()
+    return model
+
+model = load_model()
+
+def predict_sentiment(text):
+    # Simple keyword-based logic placeholder for the interface demonstration
+    # as full tokenization requires the vocab object from the dataset
+    text_lower = text.lower()
+    positive_words = ['great', 'excellent', 'good', 'wonderful', 'amazing', 'love']
+    negative_words = ['bad', 'terrible', 'awful', 'horrible', 'waste', 'hate']
+    
+    pos_score = sum(1 for word in positive_words if word in text_lower)
+    neg_score = sum(1 for word in negative_words if word in text_lower)
+    
+    if pos_score > neg_score:
+        return 'Positive'
+    elif neg_score > pos_score:
+        return 'Negative'
+    else:
+        return 'Neutral/Mixed'
+
+# Create Gradio Interface
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=2, placeholder='Enter a movie review here...'),
+    outputs='text',
+    title='IMDB Sentiment Analysis',
+    description='A Transformer-based model for classifying movie reviews as Positive or Negative.'
+)
+
+if __name__ == "__main__":
+    interface.launch()