Update classifier.py

classifier.py

@@ -1,98 +1,69 @@
-# classifier.py
-import torch
-import time
-from model_loader import classifier_model
-from paraphraser import paraphrase_comment
-from metrics import compute_semantic_similarity, compute_empathy_score, compute_bleu_score, compute_rouge_score
+# metrics.py
+import nltk
+from nltk.translate.bleu_score import sentence_bleu
+from rouge_score import rouge_scorer
+from model_loader import metrics_models
 
-def classify_toxic_comment(comment):
+# Download required NLTK data
+nltk.download('punkt')
+
+def compute_semantic_similarity(original, paraphrased):
     """
-    Classify a comment as toxic or non-toxic using the fine-tuned XLM-RoBERTa model.
-    If toxic, paraphrase the comment, re-evaluate, and compute essential metrics.
-    Returns the prediction label, confidence, color, toxicity score, bias score, paraphrased comment (if applicable), and its metrics.
+    Compute semantic similarity between the original and paraphrased comment using Sentence-BERT.
+    Returns a similarity score between 0 and 1.
     """
-    start_total = time.time()
-    print("Starting classification...")
-
-    if not comment.strip():
-        return "Error: Please enter a comment.", None, None, None, None, None, None, None, None, None, None, None, None
-
-    # Access the model and tokenizer
-    model = classifier_model.model
-    tokenizer = classifier_model.tokenizer
-
-    # Tokenize the input comment
-    start_classification = time.time()
-    inputs = tokenizer(comment, return_tensors="pt", truncation=True, padding=True, max_length=512)
-
-    # Run inference
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-
-    # Get the predicted class (0 = non-toxic, 1 = toxic)
-    predicted_class = torch.argmax(logits, dim=1).item()
-    label = "Toxic" if predicted_class == 1 else "Non-Toxic"
-    confidence = torch.softmax(logits, dim=1)[0][predicted_class].item()
-    label_color = "red" if label == "Toxic" else "green"
-
-    # Compute Toxicity Score (approximated as the probability of the toxic class)
-    toxicity_score = torch.softmax(logits, dim=1)[0][1].item()
-    toxicity_score = round(toxicity_score, 2)
-
-    # Simulate Bias Score (placeholder)
-    bias_score = 0.01 if label == "Non-Toxic" else 0.15
-    bias_score = round(bias_score, 2)
-    print(f"Classification took {time.time() - start_classification:.2f} seconds")
-
-    # If the comment is toxic, paraphrase it and compute essential metrics
-    paraphrased_comment = None
-    paraphrased_prediction = None
-    paraphrased_confidence = None
-    paraphrased_color = None
-    paraphrased_toxicity_score = None
-    paraphrased_bias_score = None
-    semantic_similarity = None
-    empathy_score = None
-    bleu_score = None
-    rouge_scores = None
+    try:
+        sentence_bert = metrics_models.load_sentence_bert()
+        embeddings = sentence_bert.encode([original, paraphrased])
+        similarity = float(embeddings[0] @ embeddings[1].T)
+        return round(similarity, 2)
+    except Exception as e:
+        print(f"Error computing semantic similarity: {str(e)}")
+        return None
 
-    if label == "Toxic":
-        # Paraphrase the comment
-        start_paraphrase = time.time()
-        paraphrased_comment = paraphrase_comment(comment)
-        print(f"Paraphrasing took {time.time() - start_paraphrase:.2f} seconds")
-
-        # Re-evaluate the paraphrased comment
-        start_reclassification = time.time()
-        paraphrased_inputs = tokenizer(paraphrased_comment, return_tensors="pt", truncation=True, padding=True, max_length=512)
-        with torch.no_grad():
-            paraphrased_outputs = model(**paraphrased_inputs)
-            paraphrased_logits = paraphrased_outputs.logits
-
-        paraphrased_predicted_class = torch.argmax(paraphrased_logits, dim=1).item()
-        paraphrased_label = "Toxic" if paraphrased_predicted_class == 1 else "Non-Toxic"
-        paraphrased_confidence = torch.softmax(paraphrased_logits, dim=1)[0][paraphrased_predicted_class].item()
-        paraphrased_color = "red" if paraphrased_label == "Toxic" else "green"
-        paraphrased_toxicity_score = torch.softmax(paraphrased_logits, dim=1)[0][1].item()
-        paraphrased_toxicity_score = round(paraphrased_toxicity_score, 2)
-        paraphrased_bias_score = 0.01 if paraphrased_label == "Non-Toxic" else 0.15  # Placeholder
-        paraphrased_bias_score = round(paraphrased_bias_score, 2)
-        print(f"Reclassification of paraphrased comment took {time.time() - start_reclassification:.2f} seconds")
-
-        # Compute essential metrics
-        start_metrics = time.time()
-        semantic_similarity = compute_semantic_similarity(comment, paraphrased_comment)
-        empathy_score = compute_empathy_score(paraphrased_comment)
-        bleu_score = compute_bleu_score(comment, paraphrased_comment)
-        rouge_scores = compute_rouge_score(comment, paraphrased_comment)
-        print(f"Metrics computation took {time.time() - start_metrics:.2f} seconds")
+def compute_empathy_score(paraphrased):
+    """
+    Compute an empathy score for the paraphrased comment (placeholder).
+    Returns a score between 0 and 1.
+    """
+    try:
+        # Placeholder: Compute empathy based on word presence (e.g., "sorry", "understand")
+        empathy_words = ["sorry", "understand", "care", "help", "support"]
+        words = paraphrased.lower().split()
+        empathy_count = sum(1 for word in words if word in empathy_words)
+        score = empathy_count / len(words) if words else 0
+        return round(score, 2)
+    except Exception as e:
+        print(f"Error computing empathy score: {str(e)}")
+        return None
 
-    print(f"Total processing time: {time.time() - start_total:.2f} seconds")
+def compute_bleu_score(original, paraphrased):
+    """
+    Compute the BLEU score between the original and paraphrased comment.
+    Returns a score between 0 and 1.
+    """
+    try:
+        reference = [nltk.word_tokenize(original.lower())]
+        candidate = nltk.word_tokenize(paraphrased.lower())
+        score = sentence_bleu(reference, candidate, weights=(0.25, 0.25, 0.25, 0.25))
+        return round(score, 2)
+    except Exception as e:
+        print(f"Error computing BLEU score: {str(e)}")
+        return None
 
-    return (
-        f"Prediction: {label}", confidence, label_color, toxicity_score, bias_score,
-        paraphrased_comment, f"Prediction: {paraphrased_label}" if paraphrased_comment else None,
-        paraphrased_confidence, paraphrased_color, paraphrased_toxicity_score, paraphrased_bias_score,
-        semantic_similarity, empathy_score, bleu_score, rouge_scores
-    )
+def compute_rouge_score(original, paraphrased):
+    """
+    Compute ROUGE scores (ROUGE-1, ROUGE-2, ROUGE-L) between the original and paraphrased comment.
+    Returns a dictionary with ROUGE scores.
+    """
+    try:
+        scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)
+        scores = scorer.score(original, paraphrased)
+        return {
+            'rouge1': round(scores['rouge1'].fmeasure, 2),
+            'rouge2': round(scores['rouge2'].fmeasure, 2),
+            'rougeL': round(scores['rougeL'].fmeasure, 2)
+        }
+    except Exception as e:
+        print(f"Error computing ROUGE scores: {str(e)}")
+        return None