Update app.py

app.py

@@ -1,94 +1,94 @@
-from flask import Flask, request, jsonify, render_template
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-import torch.nn.functional as F
-import re
-from flask_cors import CORS  # Enable CORS
-
-# Initialize Flask app
-app = Flask(__name__)
-CORS(app)  # Allow requests from frontend apps
-
-# Choose your model: 'bert-base-uncased' or 'GroNLP/hateBERT'
-MODEL_NAME = 'bert-base-uncased'  # Change to 'GroNLP/hateBERT' if needed
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-model.eval()
-
-# Two-class labels only
-LABELS = ['Safe', 'Cyberbullying']
-
-# Offensive trigger words
-TRIGGER_WORDS = [
-    "gago", "pokpok", "yawa", "linte", "ulol", "tangina", "bilat", "putang", "tarantado", "bobo",
-    "yudipota", "law-ay", "bilatibay", "hayop"
-]
-
-# Detect trigger words in input text
-def find_triggers(text):
-    found = []
-    for word in TRIGGER_WORDS:
-        if re.search(rf"\b{re.escape(word)}\b", text, re.IGNORECASE):
-            found.append(word)
-    return found
-
-# Predict function
-def predict_text(text):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-
-    # Use GPU if available
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model.to(device)
-    inputs = {key: value.to(device) for key, value in inputs.items()}
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        probs = F.softmax(logits, dim=1)
-        confidence, predicted_class = torch.max(probs, dim=1)
-
-    # Fallback logic: if model predicts more than 2 classes, default to Safe if out-of-bounds
-    label_index = predicted_class.item()
-    if label_index >= len(LABELS):
-        label_index = 0  # default to "Safe"
-
-    label = LABELS[label_index]
-    confidence_score = round(confidence.item(), 4)
-    triggers = find_triggers(text)
-
-    # Override model prediction if offensive triggers found
-    if triggers and label == "Safe":
-        label = "Cyberbullying"
-
-    return {
-        "label": label,
-        "confidence": confidence_score,
-        "triggers": triggers
-    }
-
-# Serve frontend
-@app.route('/')
-def index():
-    return render_template('index.html')  # Ensure templates/index.html exists
-
-# API endpoint
-@app.route("/predict", methods=["POST"])
-def predict_api():
-    try:
-        data = request.get_json()
-        text = data.get("text", "")
-
-        if not text.strip():
-            return jsonify({"error": "No text provided"}), 400
-
-        result = predict_text(text)
-        return jsonify(result)
-
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-# Run server
-if __name__ == "__main__":
-    app.run(debug=True)
+from flask import Flask, request, jsonify, render_template
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+import re
+from flask_cors import CORS  # Enable CORS
+
+# Initialize Flask app
+app = Flask(__name__)
+CORS(app)  # Allow requests from frontend apps
+
+# Choose your model: 'bert-base-uncased' or 'GroNLP/hateBERT'
+MODEL_NAME = 'bert-base-uncased'  # Change to 'GroNLP/hateBERT' if needed
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+model.eval()
+
+# Two-class labels only
+LABELS = ['Safe', 'Cyberbullying']
+
+# Offensive trigger words
+TRIGGER_WORDS = [
+    "gago", "pokpok", "yawa", "linte", "ulol", "tangina", "bilat", "putang", "tarantado", "bobo",
+    "yudipota", "law-ay", "bilatibay", "hayop"
+]
+
+# Detect trigger words in input text
+def find_triggers(text):
+    found = []
+    for word in TRIGGER_WORDS:
+        if re.search(rf"\b{re.escape(word)}\b", text, re.IGNORECASE):
+            found.append(word)
+    return found
+
+# Predict function
+def predict_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+    # Use GPU if available
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = F.softmax(logits, dim=1)
+        confidence, predicted_class = torch.max(probs, dim=1)
+
+    # Fallback logic: if model predicts more than 2 classes, default to Safe if out-of-bounds
+    label_index = predicted_class.item()
+    if label_index >= len(LABELS):
+        label_index = 0  # default to "Safe"
+
+    label = LABELS[label_index]
+    confidence_score = round(confidence.item(), 4)
+    triggers = find_triggers(text)
+
+    # Override model prediction if offensive triggers found
+    if triggers and label == "Safe":
+        label = "Cyberbullying"
+
+    return {
+        "label": label,
+        "confidence": confidence_score,
+        "triggers": triggers
+    }
+
+# Serve frontend
+@app.route('/')
+def index():
+    return render_template('index.html')  # Ensure templates/index.html exists
+
+# API endpoint
+@app.route("/predict", methods=["POST"])
+def predict_api():
+    try:
+        data = request.get_json()
+        text = data.get("text", "")
+
+        if not text.strip():
+            return jsonify({"error": "No text provided"}), 400
+
+        result = predict_text(text)
+        return jsonify(result)
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+# Run server
+#if __name__ == "__main__":
+#    app.run(debug=True)