Added a remotemodelwrapper class

textattack/models/wrappers/remote_model_wrapper.py

@@ -0,0 +1,61 @@
+"""
+RemoteModelWrapper class
+--------------------------
+
+"""
+
+import requests
+import torch
+import numpy as np
+import transformers
+
+class RemoteModelWrapper():
+    """This model wrapper queries a remote model with a list of text inputs.  It sends the input to a remote endpoint provided in api_url.
+
+    Args:
+        api_url (:obj:`<TYPE HERE>`): <DESCRIPTION HERE>
+    """
+    def __init__(self, api_url):
+        self.api_url = api_url
+        self.model = transformers.AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-imdb")
+
+    def __call__(self, text_input_list):
+        predictions = []
+        for text in text_input_list:
+            params = dict()
+            params["text"] = text
+            response = requests.post(self.api_url, params=params, timeout=10)  # Use POST with JSON payload
+            if response.status_code != 200:
+                print(f"Response content: {response.text}")
+                raise ValueError(f"API call failed with status {response.status_code}")
+            result = response.json()
+            # Assuming the API returns probabilities for positive and negative
+            predictions.append([result["negative"], result["positive"]])
+        return torch.tensor(predictions)
+
+"""
+Example usage: 
+
+    >>> # Define the remote model API endpoint
+    >>> api_url = "https://example.com"
+
+    >>> model_wrapper = RemoteModelWrapper(api_url)
+
+    >>> # Build the attack
+    >>> attack = textattack.attack_recipes.TextFoolerJin2019.build(model_wrapper)
+
+    >>> # Define dataset and attack arguments
+    >>> dataset = textattack.datasets.HuggingFaceDataset("imdb", split="test")
+
+    >>> attack_args = textattack.AttackArgs(
+    ...     num_examples=100,
+    ...     log_to_csv="/textfooler.csv",
+    ...     checkpoint_interval=5,
+    ...     checkpoint_dir="checkpoints", 
+    ...     disable_stdout=True
+    ... )
+
+    >>> # Run the attack
+    >>> attacker = textattack.Attacker(attack, dataset, attack_args)
+    >>> attacker.attack_dataset()
+"""