| # Imports | |
| import numpy as np | |
| from transformers import pipeline | |
| import streamlit as st | |
| import torch | |
| # Detect device automatically | |
| device = 0 if torch.cuda.is_available() else -1 | |
| tokenizer_model="distilbert-base-uncased-finetuned-sst-2-english" | |
| dataset="imdb" | |
| seed = 42 | |
| ### Load the model ------------------------------- | |
| print(f"Using {tokenizer_model} model previously trained on dataset {dataset}") | |
| model_name = "FrancescoConte/FC_finetuning-sentiment-model-3000-samples" | |
| # Define the final sentiment analysis model | |
| sentiment_model = pipeline(model=model_name, task="sentiment-analysis") | |
| ### Use the model -------------------- | |
| #try_text=["I love this move", "This movie sucks!"] | |
| #print(sentiment_model(try_text)) | |
| ### Put it on streamlit ------------------- | |
| st.title("Sentiment Analysis App") | |
| text = st.text_area("Enter text for sentiment analysis:") | |
| if st.button("Analyze"): | |
| with st.spinner("Running inference..."): | |
| result = sentiment_model(text)[0] | |
| st.success(f"**Label:** {result['label']}\n\n**Confidence:** {result['score']:.5f}") | |