add

app.py

@@ -59,28 +59,6 @@ def plot_top_repeated_words(text):
     fig = px.bar(x=words, y=counts, labels={'x': 'Words', 'y': 'Counts'}, title='Top 10 Most Repeated Words')
     st.plotly_chart(fig, use_container_width=True)
 
-
-from transformers import pipeline
-
-def is_generated_by_ai(paragraph):
-    # Load the text classification pipeline
-    text_classifier = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
-
-    # Classify the input paragraph
-    result = text_classifier(paragraph)
-
-    # You can adjust this threshold based on experimentation
-    confidence_threshold = 0.7
-
-    # Check if the label is consistent with AI-generated text
-    # label = result[0]['label']
-    confidence = result[0]['score']
-    if confidence >= confidence_threshold:
-        st.write("Confidence Score:",result[0]['score'])
-        return True
-    else:
-        return False
-
 st.set_page_config(layout="wide")
 
 st.title("GPT Shield: AI Plagiarism Detector")

perplexity.py

@@ -0,0 +1,59 @@
+import streamlit as st
+from transformers import XLNetTokenizer, XLNetLMHeadModel
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+import nltk
+from nltk.probability import FreqDist
+from collections import Counter
+from nltk.corpus import stopwords
+import string
+import numpy as np
+
+# Load XLNet tokenizer and model
+# tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
+# model = XLNetLMHeadModel.from_pretrained('xlnet-base-cased')
+
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+def calculate_perplexity(text):
+    encoded_input = tokenizer.encode(text, add_special_tokens=False, return_tensors='pt')
+    input_ids = encoded_input[0]
+    # input_ids = encoded_input[0].unsqueeze(0)
+
+
+
+    with torch.no_grad():
+        outputs = model(input_ids)
+        logits = outputs.logits
+
+    perplexity = torch.exp(torch.nn.functional.cross_entropy(logits.view(-1, logits.size(-1)), input_ids.view(-1)))
+    return perplexity.item()
+
+def generate_sample_data():
+    # Generate sample data including both human-generated and AI-generated text
+    human_text = ["we are a group of 5 engineering students, we aim to create an ai generated text detection project, that accurately marks the difference between human and GPT-generated content. This will help the audience know the origin of content, thus increase the transparency between the users."]
+
+    ai_text = ["As a team of five engineering students, we want to develop an artificial intelligence project for text identification that can distinguish between content produced by GPT and that created by humans. This will improve user transparency by assisting the audience in understanding the source of the content."]
+
+    return human_text, ai_text
+
+def calculate_threshold(human_text, ai_text):
+    # Calculate perplexity for human-generated text
+    human_perplexities = [calculate_perplexity(text) for text in human_text]
+
+    # Calculate perplexity for AI-generated text
+    ai_perplexities = [calculate_perplexity(text) for text in ai_text]
+
+    # Calculate the threshold as the mean perplexity plus some standard deviation
+    threshold = np.mean(human_perplexities) + np.std(human_perplexities)
+
+    return threshold
+
+# Generate sample data
+human_text, ai_text = generate_sample_data()
+
+# Calculate threshold
+threshold = calculate_threshold(human_text, ai_text)
+
+# Display the threshold
+st.write("Perplexity Threshold:", threshold)