Update app.py

app.py

@@ -1,20 +1,16 @@
 # Import dependencies
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, T5Tokenizer, T5ForConditionalGeneration
 import torch
 import nltk
 from nltk.corpus import wordnet
-from gensim.models import KeyedVectors
-from nltk.tokenize import word_tokenize
+import subprocess
 
 # Download NLTK data (if not already downloaded)
 nltk.download('punkt')
 nltk.download('stopwords')
 nltk.download('wordnet')  # Download WordNet
 
-# Load Word2Vec model from Gensim
-word_vectors = KeyedVectors.load_word2vec_format('path/to/GoogleNews-vectors-negative300.bin.gz', binary=True, limit=100000)  # Adjust path as needed
-
 # Check for GPU and set the device accordingly
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -22,25 +18,9 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
 model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english").to(device)
 
-# Function to get synonyms using Gensim Word2Vec
-def get_synonyms_gensim(word):
-    try:
-        synonyms = word_vectors.most_similar(positive=[word], topn=5)
-        return [synonym[0] for synonym in synonyms]
-    except KeyError:
-        return []
-
-# Paraphrasing function using Gensim for synonym replacement
-def paraphrase_text(text):
-    words = word_tokenize(text)
-    paraphrased_words = []
-    for word in words:
-        synonyms = get_synonyms_gensim(word.lower())
-        if synonyms:
-            paraphrased_words.append(synonyms[0])
-        else:
-            paraphrased_words.append(word)
-    return ' '.join(paraphrased_words)
+# Load Parrot Paraphraser model and tokenizer for humanizing text
+paraphrase_tokenizer = T5Tokenizer.from_pretrained("prithivida/parrot_paraphraser_on_T5")
+paraphrase_model = T5ForConditionalGeneration.from_pretrained("prithivida/parrot_paraphraser_on_T5").to(device)
 
 # AI detection function using DistilBERT
 def detect_ai_generated(text):
@@ -51,18 +31,43 @@ def detect_ai_generated(text):
     ai_probability = probabilities[0][1].item()  # Probability of being AI-generated
     return f"AI-Generated Content Probability: {ai_probability:.2f}%"
 
+# Humanize the AI-detected text using the Parrot Paraphraser model
+def humanize_text(AI_text):
+    inputs = paraphrase_tokenizer(AI_text, return_tensors="pt", max_length=512, truncation=True).to(device)
+    with torch.no_grad():  # Avoid gradient calculations for faster inference
+        paraphrased_ids = paraphrase_model.generate(
+            inputs['input_ids'],
+            max_length=inputs['input_ids'].shape[-1] + 20,  # Slightly more than the original input length
+            num_beams=4,
+            early_stopping=True,
+            length_penalty=1.0,
+            no_repeat_ngram_size=3,
+        )
+    paraphrased_text = paraphrase_tokenizer.decode(paraphrased_ids[0], skip_special_tokens=True)
+    return f"Humanized Text:\n{paraphrased_text}"
+
 # Gradio interface definition
-with gr.Blocks() as interface:
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(lines=5, label="Input Text")
-            detect_button = gr.Button("AI Detection")
-            paraphrase_button = gr.Button("Paraphrase Text")
-        with gr.Column():
-            output_text = gr.Textbox(label="Output")
+ai_detection_interface = gr.Interface(
+    fn=detect_ai_generated,
+    inputs="textbox",
+    outputs="text",
+    title="AI Text Detection",
+    description="Enter text to determine the probability of it being AI-generated."
+)
+
+humanization_interface = gr.Interface(
+    fn=humanize_text,
+    inputs="textbox",
+    outputs="text",
+    title="Text Humanizer",
+    description="Enter text to get a human-written version, paraphrased for natural output."
+)
 
-    detect_button.click(detect_ai_generated, inputs=text_input, outputs=output_text)
-    paraphrase_button.click(paraphrase_text, inputs=text_input, outputs=output_text)
+# Combine both interfaces into a single Gradio app with tabs
+interface = gr.TabbedInterface(
+    [ai_detection_interface, humanization_interface],
+    ["AI Detection", "Humanization"]
+)
 
 # Launch the Gradio app
 interface.launch(debug=False)