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app.py

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+import gradio as gr
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+import joblib
+import numpy as np
+
+# Load TF-IDF Vectorizer
+tfidf_vectorizer = joblib.load("models/tfidf_vectorizer.pkl")
+
+# Load the classical ML models
+lr_model = joblib.load("models/logistic_regression_tfidf.pkl")
+svm_model = joblib.load("models/svm_tfidf_model.pkl")
+nb_model = joblib.load("models/nb_tfidf_model.pkl")
+rf_model = joblib.load("models/rf_tfidf_model.pkl")
+
+# Load bert fine-tuned mmodel and tokenizer
+model_name = "tarneemalaa/bert_imdb_model"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Prediction function
+def predict_sentiment(model_picked, text, max_len=256):
+    if not text or text.strip() == "":
+        return "Please enter some text to analyze"
+    
+    # in case bert is chosen
+    if model_picked == "BERT (Fine-tuned)":
+        inputs = tokenizer(text, truncation=True, padding="max_length", max_length=max_len, return_tensors='pt')
+        input_ids = inputs['input_ids'].to(device)
+        attention_mask = inputs['attention_mask'].to(device)
+
+        with torch.no_grad():
+            output = model(input_ids=input_ids, attention_mask=attention_mask)
+            logits = output.logits
+            probs = torch.softmax(logits, dim=1)
+            pred_label = torch.argmax(probs, dim=1).item()
+            confidence = probs[0][pred_label].item()
+            confidence_display = f"{confidence:.2%}"
+
+    ## for the classical models   
+    else: 
+        vectorized = tfidf_vectorizer.transform([text])
+            
+        if model_picked == "Logistic Regression":
+            probs = lr_model.predict_proba(vectorized)[0]
+            pred_label = int(np.argmax(probs))
+            confidence = probs[pred_label]
+            confidence_display = f"{confidence:.2%}"
+                
+        elif model_picked == "SVM":
+            pred_label = int(svm_model.predict(vectorized)[0])
+            confidence_display = "<i>Not available for SVM</i>"
+                
+        elif model_picked == "Naive Bayes":
+            probs = nb_model.predict_proba(vectorized)[0]
+            pred_label = int(np.argmax(probs))
+            confidence = probs[pred_label]
+            confidence_display = f"{confidence:.2%}"
+                
+        elif model_picked == "Random Forest": 
+            probs = rf_model.predict_proba(vectorized)[0]
+            pred_label = int(np.argmax(probs))
+            confidence = probs[pred_label]
+            confidence_display = f"{confidence:.2%}"
+            
+
+    sentiment = "Positive" if pred_label == 1 else "Negative"
+    emoji = "✅" if sentiment == "Positive" else "❌"
+    color = "green" if sentiment == "Positive" else "red"
+
+    return f"""
+        <div style="font-size: 24px; font-weight: bold; color: {color}; margin-bottom: 10px;">
+            {emoji} Sentiment: {sentiment}
+        </div>
+        <div style="font-size: 18px; color: #666;">
+            Confidence: {confidence_display}
+        </div>
+        """
+
+demo = gr.Interface(
+    fn=predict_sentiment,
+    inputs=[
+        gr.Dropdown(
+            choices=[
+                "BERT (Fine-tuned)",
+                "Logistic Regression", 
+                "SVM",
+                "Naive Bayes",
+                "Random Forest"
+            ],
+            label="Choose Model",
+            value="BERT (Fine-tuned)"
+        ),
+        gr.Textbox(lines=6, placeholder="Paste a movie review here...", label="🎬 Movie Review")
+    ],
+    outputs=gr.HTML(label="Prediction Result"),
+    title="🎬 IMDb Sentiment Classifier",
+    description="This app allows you to **compare** a **fine-tuned BERT** model with **classical ML models** (Logistic Regression, SVM, Naive Bayes, Random Forest) on IMDb movie reviews.\n\nMade by [Tarneem Alaa](https://github.com/tarneemalaa1)",
+    theme=gr.themes.Soft(),
+    examples=[
+        ["BERT (Fine-tuned)", "This movie was absolutely amazing, I enjoyed every moment of it!"],
+        ["Logistic Regression", "It was a total waste of time. The plot made no sense."],
+        ["SVM", "Great acting and wonderful storyline. Highly recommend!"],
+        ["Naive Bayes", "Boring and predictable. Not worth watching."]
+    ],
+    flagging_mode="never"
+)
+
+if __name__ == "__main__":
+    demo.launch()

models.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6a5329a92001be3b64101dd58b924ed9d121497003047bbda75a9774bd6c2446
+size 18738202

requirements.txt

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+gradio
+transformers
+torch
+scikit-learn
+numpy
+joblib