Update app.py

app.py

@@ -1,81 +1,81 @@
-import torch
-import torch.nn as nn
-from transformers import DistilBertTokenizer
-import gradio as gr
-import re
-import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
-from nltk.stem import WordNetLemmatizer
-
-# Download NLTK resources
-nltk.download("stopwords")
-nltk.download("punkt_tab")
-nltk.download("wordnet")
-
-# Preprocessing setup
-stop_words = set(stopwords.words("english"))
-lemmatizer = WordNetLemmatizer()
-
-def preprocess_text(text):
-    text = re.sub(r'[^A-Za-z\s]', '', text)
-    text = re.sub(r'https?://\S+|www\.\S+', '', text)
-    text = text.lower()
-    tokens = word_tokenize(text)
-    tokens = [word for word in tokens if word not in stop_words]
-    tokens = [lemmatizer.lemmatize(word) for word in tokens]
-    return ' '.join(tokens)
-
-# GRU Classifier
-class GRUClassifier(nn.Module):
-    def __init__(self, vocab_size, embed_dim, hidden_dim, num_classes):
-        super(GRUClassifier, self).__init__()
-        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
-        self.gru = nn.GRU(embed_dim, hidden_dim, batch_first=True)
-        self.fc = nn.Linear(hidden_dim, num_classes)
-
-    def forward(self, input_ids):
-        x = self.embedding(input_ids)
-        out, _ = self.gru(x)
-        out = out[:, -1, :]
-        return self.fc(out)
-
-# Load tokenizer and model
-tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-model = GRUClassifier(
-    vocab_size=tokenizer.vocab_size,
-    embed_dim=128,
-    hidden_dim=64,
-    num_classes=2
-).to(device)
-
-model.load_state_dict(torch.load("best_gru_model.pth", map_location=device))
-model.eval()
-
-# Prediction function
-def predict_clickbait(title):
-    preprocessed = preprocess_text(title)
-    encoding = tokenizer(preprocessed, truncation=True, padding='max_length', max_length=32, return_tensors='pt')
-    input_ids = encoding['input_ids'].to(device)
-
-    with torch.no_grad():
-        output = model(input_ids)
-        pred = torch.argmax(output, dim=1).item()
-        confidence = torch.softmax(output, dim=1).squeeze()[pred].item()
-
-    label = "Clickbait" if pred == 1 else "Not Clickbait"
-    return f"{label} (Confidence: {confidence:.2f})"
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=predict_clickbait,
-    inputs=gr.Textbox(lines=2, placeholder="Enter a headline..."),
-    outputs="text",
-    title="Clickbait Title Classifier",
-    description="Detect whether a news headline is clickbait using a GRU classifier."
-)
-
-if __name__ == "__main__":
-    interface.launch()
+import torch
+import torch.nn as nn
+from transformers import DistilBertTokenizer
+import gradio as gr
+import re
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
+
+# Download NLTK resources
+nltk.download("stopwords")
+nltk.download("punkt_tab")
+nltk.download("wordnet")
+
+# Preprocessing setup
+stop_words = set(stopwords.words("english"))
+lemmatizer = WordNetLemmatizer()
+
+def preprocess_text(text):
+    text = re.sub(r'[^A-Za-z\s]', '', text)
+    text = re.sub(r'https?://\S+|www\.\S+', '', text)
+    text = text.lower()
+    tokens = word_tokenize(text)
+    tokens = [word for word in tokens if word not in stop_words]
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]
+    return ' '.join(tokens)
+
+# GRU Classifier
+class GRUClassifier(nn.Module):
+    def __init__(self, vocab_size, embed_dim, hidden_dim, num_classes):
+        super(GRUClassifier, self).__init__()
+        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
+        self.gru = nn.GRU(embed_dim, hidden_dim, batch_first=True)
+        self.fc = nn.Linear(hidden_dim, num_classes)
+
+    def forward(self, input_ids):
+        x = self.embedding(input_ids)
+        out, _ = self.gru(x)
+        out = out[:, -1, :]
+        return self.fc(out)
+
+# Load tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = GRUClassifier(
+    vocab_size=tokenizer.vocab_size,
+    embed_dim=128,
+    hidden_dim=64,
+    num_classes=2
+).to(device)
+
+model.load_state_dict(torch.load("best_gru_model.pth", map_location=device))
+model.eval()
+
+# Prediction function
+def predict_clickbait(title):
+    preprocessed = preprocess_text(title)
+    encoding = tokenizer(preprocessed, truncation=True, padding='max_length', max_length=32, return_tensors='pt')
+    input_ids = encoding['input_ids'].to(device)
+
+    with torch.no_grad():
+        output = model(input_ids)
+        pred = torch.argmax(output, dim=1).item()
+        confidence = torch.softmax(output, dim=1).squeeze()[pred].item()
+
+    label = "📢 Spam (Clickbait)" if pred == 1 else "✅ Ham (Non-Clickbait)"
+    return f"{label} (Confidence: {confidence:.2f})"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_clickbait,
+    inputs=gr.Textbox(lines=2, placeholder="Enter a news title or headline..."),
+    outputs="text",
+    title="📰 Clickbait Detector (Ham vs Spam)",
+    description="Enter a headline to detect whether it's ham (non-clickbait) or spam (clickbait) using a GRU-based model."
+)
+
+if __name__ == "__main__":
+    interface.launch()