Update app.py

app.py

@@ -1,53 +1,53 @@
-import torch
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-import gradio as gr
-import re
-import nltk
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-
-# Download necessary NLTK data
-nltk.download('punkt')
-nltk.download('stopwords')
-nltk.download('wordnet')
-
-# Preprocessing function
-def preprocess(text):
-    text = re.sub(r'[^a-zA-Z\s]', '', text).lower()
-    tokens = word_tokenize(text)
-    stop_words = set(stopwords.words('english'))
-    tokens = [word for word in tokens if word not in stop_words]
-    lemmatizer = WordNetLemmatizer()
-    tokens = [lemmatizer.lemmatize(word) for word in tokens]
-    return ' '.join(tokens)
-
-# Load tokenizer and model
-tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
-model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
-model.load_state_dict(torch.load('best_model (3).pth', map_location=torch.device('cpu')))
-model.eval()
-
-# Prediction function
-def classify_essay(text):
-    cleaned_text = preprocess(text)
-    inputs = tokenizer(cleaned_text, return_tensors='pt', truncation=True, padding=True, max_length=100)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
-        predicted_class = torch.argmax(probs, dim=1).item()
-        labels = ["Human-Written", "AI-Generated"]
-        return {labels[0]: float(probs[0][0]), labels[1]: float(probs[0][1])}
-
-# Gradio interface
-iface = gr.Interface(
-    fn=classify_essay,
-    inputs=gr.Textbox(lines=10, placeholder="Paste your essay here..."),
-    outputs=gr.Label(num_top_classes=2),
-    title="Essay Authorship Classifier",
-    description="Detect whether an essay is AI-generated or human-written using a fine-tuned DistilBERT model."
-)
-
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()
+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import gradio as gr
+import re
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Download necessary NLTK data
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# Preprocessing function
+def preprocess(text):
+    text = re.sub(r'[^a-zA-Z\s]', '', text).lower()
+    tokens = word_tokenize(text)
+    stop_words = set(stopwords.words('english'))
+    tokens = [word for word in tokens if word not in stop_words]
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]
+    return ' '.join(tokens)
+
+# Load tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
+model.load_state_dict(torch.load('best_model (3).pth', map_location=torch.device('cpu')))
+model.eval()
+
+# Prediction function
+def classify_essay(text):
+    cleaned_text = preprocess(text)
+    inputs = tokenizer(cleaned_text, return_tensors='pt', truncation=True, padding=True, max_length=100)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+        predicted_class = torch.argmax(probs, dim=1).item()
+        labels = ["Human-Written", "AI-Generated"]
+        return {labels[0]: float(probs[0][0]), labels[1]: float(probs[0][1])}
+
+# Gradio interface
+iface = gr.Interface(
+    fn=classify_essay,
+    inputs=gr.Textbox(lines=10, placeholder="Paste your essay here..."),
+    outputs=gr.Label(num_top_classes=2),
+    title="Essay Authorship Classifier",
+    description="Detect whether an essay is AI-generated or human-written using a fine-tuned DistilBERT model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()