Update gptzero_free.py

gptzero_free.py

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+"""
+This code a slight modification of perplexity by hugging face
+https://huggingface.co/docs/transformers/perplexity
+
+Both this code and the orignal code are published under the MIT license.
+
+by Burhan Ul tayyab and Nicholas Chua
+"""
+
+import torch
+import re
+from transformers import GPT2LMHeadModel, GPT2TokenizerFast
+from collections import OrderedDict
+
+
+class GPT2PPL:
+    def __init__(self, device="cuda", model_id="gpt2"):
+        self.device = device
+        self.model_id = model_id
+        self.model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
+        self.tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
+
+        self.max_length = self.model.config.n_positions
+        self.stride = 512
+        
+    def getResults(self, threshold):
+        if threshold < 60:
+            label = 0
+            return "The Text is generated by AI.", label
+        elif threshold < 80:
+            label = 0
+            return "The Text is most probably contain parts which are generated by AI. (require more text for better Judgement)", label
+        else:
+            label = 1
+            return "The Text is written by Human.", label
+
+    def __call__(self, sentence):
+        """
+        Takes in a sentence split by full stop
+        and print the perplexity of the total sentence
+
+        split the lines based on full stop and find the perplexity of each sentence and print
+        average perplexity
+
+        Burstiness is the max perplexity of each sentence
+        """
+        results = OrderedDict()
+
+        total_valid_char = re.findall("[a-zA-Z0-9]+", sentence)
+        total_valid_char = sum([len(x) for x in total_valid_char]) # finds len of all the valid characters a sentence
+
+        if total_valid_char < 100:
+            return {"status": "Please input more text (min 100 characters)"}, "Please input more text (min 100 characters)"
+        
+        lines = re.split(r'(?<=[.?!][ \[\(])|(?<=\n)\s*',sentence)
+        lines = list(filter(lambda x: (x is not None) and (len(x) > 0), lines))
+
+        ppl = self.getPPL(sentence)
+        print(f"Perplexity {ppl}")
+        results["Perplexity"] = ppl
+
+        offset = ""
+        Perplexity_per_line = []
+        for i, line in enumerate(lines):
+            if re.search("[a-zA-Z0-9]+", line) == None:
+                continue
+            if len(offset) > 0:
+                line = offset + line
+                offset = ""
+            # remove the new line pr space in the first sentence if exists
+            if line[0] == "\n" or line[0] == " ":
+                line = line[1:]
+            if line[-1] == "\n" or line[-1] == " ":
+                line = line[:-1]
+            elif line[-1] == "[" or line[-1] == "(":
+                offset = line[-1]
+                line = line[:-1]
+            ppl = self.getPPL(line)
+            Perplexity_per_line.append(ppl)
+        print(f"Perplexity per line {sum(Perplexity_per_line)/len(Perplexity_per_line)}")
+        results["Perplexity per line"] = sum(Perplexity_per_line)/len(Perplexity_per_line)
+
+        print(f"Burstiness {max(Perplexity_per_line)}")
+        results["Burstiness"] = max(Perplexity_per_line)
+
+        out, label = self.getResults(results["Perplexity per line"])
+        results["label"] = label
+
+        return results, out
+
+    def getPPL(self,sentence):
+        encodings = self.tokenizer(sentence, return_tensors="pt")
+        seq_len = encodings.input_ids.size(1)
+
+        nlls = []
+        likelihoods = []
+        prev_end_loc = 0
+        for begin_loc in range(0, seq_len, self.stride):
+            end_loc = min(begin_loc + self.max_length, seq_len)
+            trg_len = end_loc - prev_end_loc
+            input_ids = encodings.input_ids[:, begin_loc:end_loc].to(self.device)
+            target_ids = input_ids.clone()
+            target_ids[:, :-trg_len] = -100
+
+            with torch.no_grad():
+                outputs = self.model(input_ids, labels=target_ids)
+                neg_log_likelihood = outputs.loss * trg_len
+                likelihoods.append(neg_log_likelihood)
+
+            nlls.append(neg_log_likelihood)
+
+            prev_end_loc = end_loc
+            if end_loc == seq_len:
+                break
+        ppl = int(torch.exp(torch.stack(nlls).sum() / end_loc))
+        return ppl