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import os
import time
import gradio as gr
import torch
from transformers import AutoTokenizer
from model import get_sentiment, make_model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = make_model(
device=device,
tokenizer=tokenizer,
n_layers=4,
d_model=768,
num_labels=5,
n_heads=8,
dropout=0.1,
max_length=32,
)
model.to(device)
model_path = "sentiment_analysis_model.pt"
if os.path.exists(model_path):
print(f"Loading model from {model_path}...")
model.load_state_dict(torch.load(model_path, map_location=device))
else:
print("No pretrained model found. Using randomly initialized weights.")
def predict_sentiment(text):
sentiment = get_sentiment(text, model, tokenizer, device, max_length=32)
return sentiment
css_str = """
body {
background-color: #121212;
color: #e0e0e0;
}
.container {
max-width: 750px;
margin: 10px auto;
}
h1 {
font-size: 36px;
font-weight: bold;
text-align: center;
color: #ffffff;
}
.description {
font-size: 18px;
text-align: center;
color: #b0b0b0;
}
"""
with gr.Blocks(css=css_str) as demo:
gr.HTML("<div class='container'>")
gr.Markdown("<h1>Sentiment Analysis</h1>")
gr.Markdown(
"<div class='description'>Enter a sentence and see the predicted sentiment.</div>"
)
text_input = gr.Textbox(
label="Enter Text", lines=3, placeholder="Type your review or sentence here..."
)
predict_btn = gr.Button("Predict Sentiment")
output_box = gr.Textbox(label="Predicted Sentiment")
predict_btn.click(fn=predict_sentiment, inputs=text_input, outputs=output_box)
gr.HTML("</div>")
if __name__ == "__main__":
demo.launch(share=True)
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