DivEye - PR (fixes x3) (#11)

- contingous? (08abcbbe0bb86f2fb43dca2a0204b700da395312)
- global vars (f776ced53467ad04cffe7180d8c7aae44026c4ce)

app.py

@@ -5,11 +5,118 @@ import pandas as pd
 from software import Software
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+import xgboost as xgb
+import pandas as pd
+import numpy as np
+import torch
+import zlib
+from scipy.stats import skew, kurtosis, entropy
+from tqdm import tqdm
+from torch.nn import CrossEntropyLoss
+from pathlib import Path
+import spaces
+import os
 
 theme = gr.Theme.from_hub("gstaff/xkcd") 
 
+class Diversity:
+    def __init__(self, model, tokenizer, device):
+        self.tokenizer = tokenizer
+        self.model = model
+        self.device = device
+        
+    def compute_log_likelihoods(self, text):
+        tokens = self.tokenizer.encode(text, return_tensors="pt", truncation=True, max_length=1024).to(self.device)
+        with torch.no_grad():
+            outputs = self.model(tokens, labels=tokens)
+        logits = outputs.logits
+        shift_logits = logits[:, :-1, :].squeeze(0)
+        shift_labels = tokens[:, 1:].squeeze(0)
+        log_probs = torch.log_softmax(shift_logits.float(), dim=-1)
+        token_log_likelihoods = log_probs[range(shift_labels.shape[0]), shift_labels].cpu().numpy()
+        return token_log_likelihoods
+    
+    def compute_surprisal(self, text):
+        log_likelihoods = self.compute_log_likelihoods(text)
+        return -log_likelihoods
+        
+    def compute_features(self, text):
+        surprisals = self.compute_surprisal(text)
+        log_likelihoods = self.compute_log_likelihoods(text)
+        if len(surprisals) < 10 or len(log_likelihoods) < 3:
+            return None
+
+        s = np.array(surprisals)
+        mean_s, std_s, var_s, skew_s, kurt_s = np.mean(s), np.std(s), np.var(s), skew(s), kurtosis(s)
+        diff_s = np.diff(s)
+        mean_diff, std_diff = np.mean(diff_s), np.std(diff_s)
+        first_order_diff = np.diff(log_likelihoods)
+        second_order_diff = np.diff(first_order_diff)
+        var_2nd = np.var(second_order_diff)
+        entropy_2nd = entropy(np.histogram(second_order_diff, bins=20, density=True)[0])
+        autocorr_2nd = np.corrcoef(second_order_diff[:-1], second_order_diff[1:])[0, 1] if len(second_order_diff) > 1 else 0
+        comp_ratio = len(zlib.compress(text.encode('utf-8'))) / len(text.encode('utf-8'))
+
+        return [mean_s, std_s, var_s, skew_s, kurt_s, mean_diff, std_diff, var_2nd, entropy_2nd, autocorr_2nd, comp_ratio]
+
+class BiScope:
+    def __init__(self, model, tokenizer, device):
+        self.COMPLETION_PROMPT_ONLY = "Complete the following text: "
+        self.tokenizer = tokenizer
+        self.model = model
+        self.device = device
+    
+    def compute_fce_loss(self, logits, targets, text_slice):
+        return CrossEntropyLoss(reduction='none')(
+            logits[0, text_slice.start-1:text_slice.stop-1, :],
+            targets
+        ).detach().cpu().numpy()
+    
+    def compute_bce_loss(self, logits, targets, text_slice):
+        return CrossEntropyLoss(reduction='none')(
+            logits[0, text_slice, :],
+            targets
+        ).detach().cpu().numpy()
+    
+    def detect_single_sample(self, sample):
+        prompt_ids = self.tokenizer(self.COMPLETION_PROMPT_ONLY, return_tensors='pt').input_ids.to(self.device)
+        text_ids = self.tokenizer(sample, return_tensors='pt', max_length=2000, truncation=True).input_ids.to(self.device)
+        combined_ids = torch.cat([prompt_ids, text_ids], dim=1)
+        text_slice = slice(prompt_ids.shape[1], combined_ids.shape[1])
+
+        outputs = self.model(input_ids=combined_ids)
+        logits = outputs.logits
+        targets = combined_ids[0][text_slice]
+        
+        fce_loss = self.compute_fce_loss(logits, targets, text_slice)
+        bce_loss = self.compute_bce_loss(logits, targets, text_slice)
+
+        features = []
+        for p in range(1, 10):
+            split = len(fce_loss) * p // 10
+            fce_clipped = np.nan_to_num(np.clip(fce_loss[split:], -1e6, 1e6), nan=0.0, posinf=1e6, neginf=-1e6)
+            bce_clipped = np.nan_to_num(np.clip(bce_loss[split:], -1e6, 1e6), nan=0.0, posinf=1e6, neginf=-1e6)
+            features.extend([
+                np.mean(fce_clipped), np.max(fce_clipped), np.min(fce_clipped), np.std(fce_clipped),
+                np.mean(bce_clipped), np.max(bce_clipped), np.min(bce_clipped), np.std(bce_clipped)
+            ])
+        return features
+
+# ===========================================================
+@spaces.GPU
+def evaluate(diveye, biscope, text):
+    global model
+    diveye_features = diveye.compute_features(text)
+    biscope_features = biscope.detect_single_sample(text)
+
+    for f in biscope_features:
+        diveye_features.append(f)
+
+    return model.predict_proba([diveye_features])[:, 1][0].item()
+
 def detect_ai_text(text):
-    if software is None:
+    global loaded, diveye, biscope, model
+    if not loaded:
         return "❗ Model not loaded. We require a GPU to run DivEye.", 0.0, pd.DataFrame({
             "Source": ["AI Generated", "Human Written"],
             "Probability (%)": [0, 0]
@@ -27,7 +134,7 @@ def detect_ai_text(text):
         )
 
     # Call software
-    ai_prob = software.evaluate(text)
+    ai_prob = evaluate(diveye, biscope, text)
     human_prob = 1 - ai_prob
 
     if ai_prob > 0.7:
@@ -44,15 +151,18 @@ def detect_ai_text(text):
 
     return message, round(ai_prob, 3), bar_data
 
+# ==========================================================
 # Token from environment variable
 token = os.getenv("HF_TOKEN") 
+loaded = False
 
 if not torch.cuda.is_available():
+    loaded = False
     print("[DivEye] CUDA not available. Running on CPU.")
-    DESCRIPTION = "This demo requires a GPU to run efficiently. Please use a machine with CUDA support."
 
 # Import necessary models and tokenizers
 if torch.cuda.is_available():
+    loaded = True
     model_name_div = "tiiuae/falcon-7b"
     model_name_bi = "google/gemma-1.1-2b-it"
 
@@ -64,8 +174,13 @@ if torch.cuda.is_available():
 
     div_model.eval()
     bi_model.eval()
+    
+    model_path = Path(__file__).parent / "model.json"
+    model = xgb.XGBClassifier()
+    model.load_model(model_path)
 
-    software = Software(div_model, div_tokenizer, bi_model, bi_tokenizer, div_model.device, bi_model.device)
+    diveye = Diversity(div_model, div_tokenizer, div_model.device)
+    biscope = BiScope(bi_model, bi_tokenizer, bi_model.device)
 
 # Gradio app setup
 with gr.Blocks(title="DivEye") as demo:

software.py

@@ -1,125 +0,0 @@
-import json
-import xgboost as xgb
-import pandas as pd
-import numpy as np
-import torch
-import zlib
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from scipy.stats import skew, kurtosis, entropy
-from tqdm import tqdm
-from torch.nn import CrossEntropyLoss
-from pathlib import Path
-import spaces
-import os
-
-class Diversity:
-    def __init__(self, model, tokenizer, device):
-        self.tokenizer = tokenizer
-        self.model = model
-        self.device = device
-        
-    def compute_log_likelihoods(self, text):
-        tokens = self.tokenizer.encode(text, return_tensors="pt", truncation=True, max_length=1024).to(self.device)
-        with torch.no_grad():
-            outputs = self.model(tokens, labels=tokens)
-        logits = outputs.logits
-        shift_logits = logits[:, :-1, :].squeeze(0)
-        shift_labels = tokens[:, 1:].squeeze(0)
-        log_probs = torch.log_softmax(shift_logits.float(), dim=-1)
-        token_log_likelihoods = log_probs[range(shift_labels.shape[0]), shift_labels].cpu().numpy()
-        return token_log_likelihoods
-    
-    def compute_surprisal(self, text):
-        log_likelihoods = self.compute_log_likelihoods(text)
-        return -log_likelihoods
-        
-    def compute_features(self, text):
-        surprisals = self.compute_surprisal(text)
-        log_likelihoods = self.compute_log_likelihoods(text)
-        if len(surprisals) < 10 or len(log_likelihoods) < 3:
-            return None
-
-        s = np.array(surprisals)
-        mean_s, std_s, var_s, skew_s, kurt_s = np.mean(s), np.std(s), np.var(s), skew(s), kurtosis(s)
-        diff_s = np.diff(s)
-        mean_diff, std_diff = np.mean(diff_s), np.std(diff_s)
-        first_order_diff = np.diff(log_likelihoods)
-        second_order_diff = np.diff(first_order_diff)
-        var_2nd = np.var(second_order_diff)
-        entropy_2nd = entropy(np.histogram(second_order_diff, bins=20, density=True)[0])
-        autocorr_2nd = np.corrcoef(second_order_diff[:-1], second_order_diff[1:])[0, 1] if len(second_order_diff) > 1 else 0
-        comp_ratio = len(zlib.compress(text.encode('utf-8'))) / len(text.encode('utf-8'))
-
-        return [mean_s, std_s, var_s, skew_s, kurt_s, mean_diff, std_diff, var_2nd, entropy_2nd, autocorr_2nd, comp_ratio]
-
-class BiScope:
-    def __init__(self, model, tokenizer, device):
-        self.COMPLETION_PROMPT_ONLY = "Complete the following text: "
-        self.tokenizer = tokenizer
-        self.model = model
-        self.device = device
-    
-    def compute_fce_loss(self, logits, targets, text_slice):
-        return CrossEntropyLoss(reduction='none')(
-            logits[0, text_slice.start-1:text_slice.stop-1, :],
-            targets
-        ).detach().cpu().numpy()
-    
-    def compute_bce_loss(self, logits, targets, text_slice):
-        return CrossEntropyLoss(reduction='none')(
-            logits[0, text_slice, :],
-            targets
-        ).detach().cpu().numpy()
-    
-    def detect_single_sample(self, sample):
-        prompt_ids = self.tokenizer(self.COMPLETION_PROMPT_ONLY, return_tensors='pt').input_ids.to(self.device)
-        text_ids = self.tokenizer(sample, return_tensors='pt', max_length=2000, truncation=True).input_ids.to(self.device)
-        combined_ids = torch.cat([prompt_ids, text_ids], dim=1)
-        text_slice = slice(prompt_ids.shape[1], combined_ids.shape[1])
-
-        outputs = self.model(input_ids=combined_ids)
-        logits = outputs.logits
-        targets = combined_ids[0][text_slice]
-        
-        fce_loss = self.compute_fce_loss(logits, targets, text_slice)
-        bce_loss = self.compute_bce_loss(logits, targets, text_slice)
-
-        features = []
-        for p in range(1, 10):
-            split = len(fce_loss) * p // 10
-            fce_clipped = np.nan_to_num(np.clip(fce_loss[split:], -1e6, 1e6), nan=0.0, posinf=1e6, neginf=-1e6)
-            bce_clipped = np.nan_to_num(np.clip(bce_loss[split:], -1e6, 1e6), nan=0.0, posinf=1e6, neginf=-1e6)
-            features.extend([
-                np.mean(fce_clipped), np.max(fce_clipped), np.min(fce_clipped), np.std(fce_clipped),
-                np.mean(bce_clipped), np.max(bce_clipped), np.min(bce_clipped), np.std(bce_clipped)
-            ])
-        return features
-
-
-class Software:
-    def __init__(self, div_model, div_tokenizer, bi_model, bi_tokenizer, device_div="cuda", device_bi="cuda"):
-        self.div_model = div_model
-        self.div_tokenizer = div_tokenizer
-        self.bi_model = bi_model
-        self.bi_tokenizer = bi_tokenizer 
-
-        self.device_div = device_div
-        self.device_bi = device_bi    
-        
-        self.model_path = Path(__file__).parent / "model.json"
-
-        self.model = xgb.XGBClassifier()
-        self.model.load_model(self.model_path)
-    
-    @spaces.GPU
-    def evaluate(self, text):     
-        diveye = Diversity(self.div_model, self.div_tokenizer, self.device_div)
-        biscope = BiScope(self.bi_model, self.bi_tokenizer, self.device_bi)
-
-        diveye_features = diveye.compute_features(text)
-        biscope_features = biscope.detect_single_sample(text)
-
-        for f in biscope_features:
-            diveye_features.append(f)
-    
-        return self.model.predict_proba([diveye_features])[:, 1][0].item()