Create app.py

app.py

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+from flask import Flask, request
+import gradio as gr
+import os
+import re
+app = Flask(__name__)
+
+import torch
+from tqdm import tqdm
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+
+device = 'cuda' if cuda.is_available() else 'cpu'
+model_id = "gpt2"
+modelgpt2 = GPT2LMHeadModel.from_pretrained(model_id).to(device)
+tokenizergpt2 = GPT2TokenizerFast.from_pretrained(model_id)
+
+def text_to_sentences(text):
+    clean_text = text.replace('\n', ' ')
+    return re.split(r'(?<=[^A-Z].[.?]) +(?=[A-Z])', clean_text)
+
+def calculatePerplexity(text):
+    encodings = tokenizergpt2("\n\n".join([text]), return_tensors="pt")
+    max_length = modelgpt2.config.n_positions
+    stride = 512
+    input_ids = encodings.input_ids
+    seq_len = input_ids.size(1)
+
+    nlls = []
+    prev_end_loc = 0
+    for begin_loc in range(0, seq_len, stride):
+        end_loc = min(begin_loc + max_length, seq_len)
+        trg_len = end_loc - prev_end_loc
+        target_ids = input_ids.clone()
+        target_ids[:, :-trg_len] = -100
+
+        with torch.no_grad():
+            outputs = modelgpt2(input_ids, labels=target_ids)
+            neg_log_likelihood = outputs.loss * trg_len
+
+        nlls.append(neg_log_likelihood)
+
+        prev_end_loc = end_loc
+        if end_loc == seq_len:
+            break
+
+    ppl = torch.exp(torch.stack(nlls).sum() / end_loc)
+
+    return ppl.item()
+    
+def calculatePerplexities(text):
+    sentences = text_to_sentences(text)
+    perplexities = []
+    for sentence in sentences:
+        perplexities.append(calculatePerplexity(sentence))
+    return perplexities
+
+demo = gr.Interface(
+        fn=calculatePerplexities, 
+        inputs=gr.Textbox(placeholder="Copy and paste here..."), 
+        article = "Visit <a href = \"https://ai-content-detector.online/\">AI Content Detector</a> for better user experience!",
+        outputs=gr.outputs.JSON(),
+        interpretation="default",
+
+demo.launch(show_api=False