From 89a04d54fcd3b667ef0ea5474709ffb86845f081 Mon Sep 17 00:00:00 2001
From: l3ra <lera2mail@gmail.com>
Date: Mon, 30 Dec 2024 15:37:19 +0000
Subject: [PATCH] Added a remotemodelwrapper class

---
 .../models/wrappers/remote_model_wrapper.py   | 63 +++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 textattack/models/wrappers/remote_model_wrapper.py

diff --git a/textattack/models/wrappers/remote_model_wrapper.py b/textattack/models/wrappers/remote_model_wrapper.py
new file mode 100644
index 00000000..0a0dc046
--- /dev/null
+++ b/textattack/models/wrappers/remote_model_wrapper.py
@@ -0,0 +1,63 @@
+"""
+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.
+
+
+    """
+    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 and tokenizer
+api_url = "https://x.com/predict"
+
+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()
+
+'''
\ No newline at end of file