import os os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # Disable GPU and enforce CPU execution from PIL import Image from huggingface_hub import hf_hub_download # Load a fun unicorn image unicorn_image_path = "unicorn.png" import gradio as gr from transformers import ( DistilBertTokenizerFast, DistilBertForSequenceClassification, AutoTokenizer, AutoModelForSequenceClassification, ) from huggingface_hub import hf_hub_download import torch import pickle import numpy as np from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing.sequence import pad_sequences import re # Load GRU, LSTM, and BiLSTM models and tokenizers gru_repo_id = "arjahojnik/GRU-sentiment-model" gru_model_path = hf_hub_download(repo_id=gru_repo_id, filename="best_GRU_tuning_model.h5") gru_model = load_model(gru_model_path) gru_tokenizer_path = hf_hub_download(repo_id=gru_repo_id, filename="my_tokenizer.pkl") with open(gru_tokenizer_path, "rb") as f: gru_tokenizer = pickle.load(f) lstm_repo_id = "arjahojnik/LSTM-sentiment-model" lstm_model_path = hf_hub_download(repo_id=lstm_repo_id, filename="LSTM_model.h5") lstm_model = load_model(lstm_model_path) lstm_tokenizer_path = hf_hub_download(repo_id=lstm_repo_id, filename="my_tokenizer.pkl") with open(lstm_tokenizer_path, "rb") as f: lstm_tokenizer = pickle.load(f) bilstm_repo_id = "arjahojnik/BiLSTM-sentiment-model" bilstm_model_path = hf_hub_download(repo_id=bilstm_repo_id, filename="BiLSTM_model.h5") bilstm_model = load_model(bilstm_model_path) bilstm_tokenizer_path = hf_hub_download(repo_id=bilstm_repo_id, filename="my_tokenizer.pkl") with open(bilstm_tokenizer_path, "rb") as f: bilstm_tokenizer = pickle.load(f) # Preprocessing function for text def preprocess_text(text): text = text.lower() text = re.sub(r"[^a-zA-Z\s]", "", text).strip() return text # Prediction functions for GRU, LSTM, and BiLSTM def predict_with_gru(text): cleaned = preprocess_text(text) seq = gru_tokenizer.texts_to_sequences([cleaned]) padded_seq = pad_sequences(seq, maxlen=200) probs = gru_model.predict(padded_seq) predicted_class = np.argmax(probs, axis=1)[0] return int(predicted_class + 1) def predict_with_lstm(text): cleaned = preprocess_text(text) seq = lstm_tokenizer.texts_to_sequences([cleaned]) padded_seq = pad_sequences(seq, maxlen=200) probs = lstm_model.predict(padded_seq) predicted_class = np.argmax(probs, axis=1)[0] return int(predicted_class + 1) def predict_with_bilstm(text): cleaned = preprocess_text(text) seq = bilstm_tokenizer.texts_to_sequences([cleaned]) padded_seq = pad_sequences(seq, maxlen=200) probs = bilstm_model.predict(padded_seq) predicted_class = np.argmax(probs, axis=1)[0] return int(predicted_class + 1) # Load other models models = { "DistilBERT": { "tokenizer": DistilBertTokenizerFast.from_pretrained("nhull/distilbert-sentiment-model"), "model": DistilBertForSequenceClassification.from_pretrained("nhull/distilbert-sentiment-model"), }, "Logistic Regression": {}, # Placeholder for logistic regression "BERT Multilingual (NLP Town)": { "tokenizer": AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment"), "model": AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment"), }, "TinyBERT": { "tokenizer": AutoTokenizer.from_pretrained("elo4/TinyBERT-sentiment-model"), "model": AutoModelForSequenceClassification.from_pretrained("elo4/TinyBERT-sentiment-model"), }, "RoBERTa": { "tokenizer": AutoTokenizer.from_pretrained("ordek899/roberta_1to5rating_pred_for_restaur_trained_on_hotels"), "model": AutoModelForSequenceClassification.from_pretrained("ordek899/roberta_1to5rating_pred_for_restaur_trained_on_hotels"), } } # Logistic regression model and TF-IDF vectorizer logistic_regression_repo = "nhull/logistic-regression-model" log_reg_model_path = hf_hub_download(repo_id=logistic_regression_repo, filename="logistic_regression_model.pkl") with open(log_reg_model_path, "rb") as model_file: log_reg_model = pickle.load(model_file) vectorizer_path = hf_hub_download(repo_id=logistic_regression_repo, filename="tfidf_vectorizer.pkl") with open(vectorizer_path, "rb") as vectorizer_file: vectorizer = pickle.load(vectorizer_file) # Move HuggingFace models to device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") for model_data in models.values(): if "model" in model_data: model_data["model"].to(device) # Prediction functions for other models def predict_with_distilbert(text): tokenizer = models["DistilBERT"]["tokenizer"] model = models["DistilBERT"]["model"] encodings = tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device) with torch.no_grad(): outputs = model(**encodings) logits = outputs.logits predictions = logits.argmax(axis=-1).cpu().numpy() return int(predictions[0] + 1) def predict_with_logistic_regression(text): transformed_text = vectorizer.transform([text]) predictions = log_reg_model.predict(transformed_text) return int(predictions[0]) def predict_with_bert_multilingual(text): tokenizer = models["BERT Multilingual (NLP Town)"]["tokenizer"] model = models["BERT Multilingual (NLP Town)"]["model"] encodings = tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device) with torch.no_grad(): outputs = model(**encodings) logits = outputs.logits predictions = logits.argmax(axis=-1).cpu().numpy() return int(predictions[0] + 1) def predict_with_tinybert(text): tokenizer = models["TinyBERT"]["tokenizer"] model = models["TinyBERT"]["model"] encodings = tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device) with torch.no_grad(): outputs = model(**encodings) logits = outputs.logits predictions = logits.argmax(axis=-1).cpu().numpy() return int(predictions[0] + 1) def predict_with_roberta_ordek899(text): tokenizer = models["RoBERTa"]["tokenizer"] model = models["RoBERTa"]["model"] encodings = tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device) with torch.no_grad(): outputs = model(**encodings) logits = outputs.logits predictions = logits.argmax(axis=-1).cpu().numpy() return int(predictions[0] + 1) # Unified function for analysis def analyze_sentiment_and_statistics(text): results = { "Logistic Regression": predict_with_logistic_regression(text), "GRU Model": predict_with_gru(text), "LSTM Model": predict_with_lstm(text), "BiLSTM Model": predict_with_bilstm(text), "DistilBERT": predict_with_distilbert(text), "BERT Multilingual (NLP Town)": predict_with_bert_multilingual(text), "TinyBERT": predict_with_tinybert(text), "RoBERTa": predict_with_roberta_ordek899(text), } scores = list(results.values()) min_score = min(scores) max_score = max(scores) min_score_models = [model for model, score in results.items() if score == min_score] max_score_models = [model for model, score in results.items() if score == max_score] average_score = np.mean(scores) if all(score == scores[0] for score in scores): statistics = { "Message": "All models predict the same score.", "Average Score": f"{average_score:.2f}", } else: statistics = { "Lowest Score": f"{min_score} (Models: {', '.join(min_score_models)})", "Highest Score": f"{max_score} (Models: {', '.join(max_score_models)})", "Average Score": f"{average_score:.2f}", } return results, statistics # Gradio Interface with gr.Blocks( css=""" .gradio-container { max-width: 900px; margin: auto; padding: 20px; } h1 { text-align: center; font-size: 2.5rem; } .unicorn-image { display: block; margin: auto; width: 300px; /* Larger size */ height: auto; border-radius: 20px; margin-bottom: 20px; animation: magical-float 5s ease-in-out infinite; /* Gentle floating animation */ } @keyframes magical-float { 0% { transform: translate(0, 0) rotate(0deg); /* Start position */ } 25% { transform: translate(10px, -10px) rotate(3deg); /* Slightly up and right, tilted */ } 50% { transform: translate(0, -20px) rotate(0deg); /* Higher point, back to straight */ } 75% { transform: translate(-10px, -10px) rotate(-3deg); /* Slightly up and left, tilted */ } 100% { transform: translate(0, 0) rotate(0deg); /* Return to start position */ } } footer { text-align: center; margin-top: 20px; font-size: 14px; color: gray; } .custom-analyze-button { background-color: #e8a4c9; color: white; font-size: 1rem; padding: 10px 20px; border-radius: 10px; border: none; box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); transition: transform 0.2s, background-color 0.2s; } .custom-analyze-button:hover { background-color: #d693b8; transform: scale(1.05); } """ ) as demo: # Add the unicorn image at the start gr.Image( value=unicorn_image_path, # File path or URL type="filepath", # Correct type for file paths elem_classes=["unicorn-image"] ) gr.Markdown("# Sentiment Analysis Demo") gr.Markdown( """ Welcome! A magical unicorn 🦄 will guide you through this sentiment analysis journey! 🎉 This app lets you explore how different models interpret sentiment and compare their predictions. **Enjoy the magic!** """ ) with gr.Row(): with gr.Column(): text_input = gr.Textbox( label="Enter your text here:", lines=3, placeholder="Type your hotel/restaurant review here..." ) sample_reviews = [ "The hotel was fantastic! Clean rooms and excellent service.", "The food was horrible, and the staff was rude.", "Amazing experience overall. Highly recommend!", "It was okay, not great but not terrible either.", "Terrible! The room was dirty, and the service was non-existent." ] sample_dropdown = gr.Dropdown( choices=sample_reviews, label="Or select a sample review:", interactive=True ) def update_textbox(selected_sample): return selected_sample sample_dropdown.change( update_textbox, inputs=[sample_dropdown], outputs=[text_input] ) analyze_button = gr.Button("Analyze Sentiment", elem_classes=["custom-analyze-button"]) with gr.Row(): with gr.Column(): gr.Markdown("### Machine Learning") log_reg_output = gr.Textbox(label="Logistic Regression", interactive=False) with gr.Column(): gr.Markdown("### Deep Learning") gru_output = gr.Textbox(label="GRU Model", interactive=False) lstm_output = gr.Textbox(label="LSTM Model", interactive=False) bilstm_output = gr.Textbox(label="BiLSTM Model", interactive=False) with gr.Column(): gr.Markdown("### Transformers") distilbert_output = gr.Textbox(label="DistilBERT", interactive=False) bert_output = gr.Textbox(label="BERT Multilingual", interactive=False) tinybert_output = gr.Textbox(label="TinyBERT", interactive=False) roberta_output = gr.Textbox(label="RoBERTa", interactive=False) with gr.Row(): with gr.Column(): gr.Markdown("### Statistics") stats_output = gr.Textbox(label="Statistics", interactive=False) # Add footer gr.Markdown( """ """ ) def process_input_and_analyze(text_input): # Check for empty input if not text_input.strip(): funny_message = "Are you sure you wrote something? Try again! 🧐" return ( funny_message, # Logistic Regression funny_message, # GRU funny_message, # LSTM funny_message, # BiLSTM funny_message, # DistilBERT funny_message, # BERT Multilingual funny_message, # TinyBERT funny_message, # RoBERTa "No statistics to display, as nothing was input. 🤷‍♀️" ) # Check for one letter/number input if len(text_input.strip()) == 1 or text_input.strip().isdigit(): funny_message = "Why not write something that makes sense? 🤔" return ( funny_message, # Logistic Regression funny_message, # GRU funny_message, # LSTM funny_message, # BiLSTM funny_message, # DistilBERT funny_message, # BERT Multilingual funny_message, # TinyBERT funny_message, # RoBERTa "No statistics to display for one letter or number. 😅" ) # Check if the review is shorter than 5 words if len(text_input.split()) < 5: results, statistics = analyze_sentiment_and_statistics(text_input) short_message = "Maybe try with some longer text next time. 😉" return ( f"{results['Logistic Regression']} - {short_message}", f"{results['GRU Model']} - {short_message}", f"{results['LSTM Model']} - {short_message}", f"{results['BiLSTM Model']} - {short_message}", f"{results['DistilBERT']} - {short_message}", f"{results['BERT Multilingual (NLP Town)']} - {short_message}", f"{results['TinyBERT']} - {short_message}", f"{results['RoBERTa']} - {short_message}", f"Statistics:\n{statistics['Lowest Score']}\n{statistics['Highest Score']}\nAverage Score: {statistics['Average Score']}\n{short_message}" ) # Proceed with normal sentiment analysis if none of the above conditions apply results, statistics = analyze_sentiment_and_statistics(text_input) if "Message" in statistics: return ( results["Logistic Regression"], results["GRU Model"], results["LSTM Model"], results["BiLSTM Model"], results["DistilBERT"], results["BERT Multilingual (NLP Town)"], results["TinyBERT"], results["RoBERTa"], f"Statistics:\n{statistics['Message']}\nAverage Score: {statistics['Average Score']}" ) else: return ( results["Logistic Regression"], results["GRU Model"], results["LSTM Model"], results["BiLSTM Model"], results["DistilBERT"], results["BERT Multilingual (NLP Town)"], results["TinyBERT"], results["RoBERTa"], f"Statistics:\n{statistics['Lowest Score']}\n{statistics['Highest Score']}\nAverage Score: {statistics['Average Score']}" ) analyze_button.click( process_input_and_analyze, inputs=[text_input], outputs=[ log_reg_output, gru_output, lstm_output, bilstm_output, distilbert_output, bert_output, tinybert_output, roberta_output, stats_output ] ) demo.launch()