Create demo.py

demo.py

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+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+from huggingface_hub import hf_hub_download
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+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 SVM model and vectorizer
+svm_repo_id = "your-username/svm-sentiment-model"  # Replace with your actual repo
+svm_model_path = hf_hub_download(repo_id=svm_repo_id, filename="svm_model.pkl")
+with open(svm_model_path, "rb") as f:
+    svm_model = pickle.load(f)
+vectorizer_path = hf_hub_download(repo_id=svm_repo_id, filename="vectorizer.pkl")
+with open(vectorizer_path, "rb") as f:
+    vectorizer = pickle.load(f)
+
+# Load LSTM model and tokenizer
+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)
+
+# Load BERT model and tokenizer
+bert_tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+bert_model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+bert_model.to(device)
+
+def preprocess_text(text):
+    text = text.lower()
+    text = re.sub(r"[^a-zA-Z\s]", "", text).strip()
+    return text
+
+def predict_with_svm(text):
+    transformed = vectorizer.transform([text])
+    prediction = svm_model.predict(transformed)
+    return int(prediction[0])
+
+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_bert(text):
+    inputs = bert_tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)
+    with torch.no_grad():
+        outputs = bert_model(**inputs)
+        logits = outputs.logits
+        predictions = logits.argmax(axis=-1).cpu().numpy()
+    return int(predictions[0] + 1)
+
+def analyze_sentiment(text):
+    results = {
+        "SVM": predict_with_svm(text),
+        "LSTM": predict_with_lstm(text),
+        "BERT": predict_with_bert(text)
+    }
+    scores = list(results.values())
+    average = np.mean(scores)
+    stats = f"Average Score: {average:.2f}\n"
+    return (
+        convert_to_stars(results["SVM"]),
+        convert_to_stars(results["LSTM"]),
+        convert_to_stars(results["BERT"]),
+        stats
+    )
+
+def convert_to_stars(score):
+    return "★" * score + "☆" * (5 - score)
+
+def process_input(text):
+    if not text.strip():
+        return ("", "", "", "Please enter valid text.")
+    return analyze_sentiment(text)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Sentiment Analysis Demo")
+    gr.Markdown("""
+        Enter a review and see how different models evaluate its sentiment! This app uses:
+        - SVM for classic machine learning
+        - LSTM for deep learning
+        - BERT for transformer-based analysis
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Enter your review:", lines=3)
+            analyze_button = gr.Button("Analyze Sentiment")
+
+        with gr.Column():
+            svm_output = gr.Textbox(label="SVM", interactive=False)
+            lstm_output = gr.Textbox(label="LSTM", interactive=False)
+            bert_output = gr.Textbox(label="BERT", interactive=False)
+            stats_output = gr.Textbox(label="Statistics", interactive=False)
+
+    analyze_button.click(
+        process_input,
+        inputs=[input_text],
+        outputs=[svm_output, lstm_output, bert_output, stats_output]
+    )
+
+demo.launch()