init

app.py

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+import gradio as gr
+import time
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+label_dict = {"Urgency": 0, "Not Dark Pattern": 1, "Scarcity": 2, "Misdirection": 3, "Social Proof": 4, "Obstruction": 5, "Sneaking": 6, "Forced Action": 7}
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=len(label_dict))
+fine_tuned_model_path = "models/finetuned_BERT_epoch_5.model"
+model.load_state_dict(torch.load(fine_tuned_model_path, map_location=torch.device('cpu')))
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
+
+def get_dark_pattern_name(label):
+    reverse_label_dict = {v: k for k, v in label_dict.items()}
+    return reverse_label_dict[label]
+
+def find_dark_pattern(text_predict):
+    encoded_text = tokenizer.encode_plus(
+        text_predict,
+        add_special_tokens=True,
+        return_attention_mask=True,
+        pad_to_max_length=True,
+        max_length=256,
+        return_tensors='pt'
+    )
+
+    model.eval()
+
+    with torch.no_grad():
+        inputs = {
+            'input_ids': encoded_text['input_ids'],
+            'attention_mask': encoded_text['attention_mask']
+        }
+        outputs = model(**inputs)
+
+    predictions = outputs.logits
+
+    probabilities = torch.nn.functional.softmax(predictions, dim=1)
+    predicted_label = torch.argmax(probabilities, dim=1).item()
+
+    return get_dark_pattern_name(predicted_label)
+
+def predict(text_to_predict):
+    start_time = time.time()
+    print("Predicting Dark Pattern...")
+    for i in range(10):
+        predicted_darkp = find_dark_pattern(text_to_predict)
+        time.sleep(0.5)
+    end_time = time.time()
+    total_time = end_time - start_time
+    return f"Result: {predicted_darkp}\nTotal Time Taken: {total_time:.2f} seconds"
+
+demo = gr.Interface(fn=predict, inputs="text", outputs="text")
+demo.launch(share=True)
+
+

requirements.txt

@@ -0,0 +1,110 @@
+aiofiles==23.2.1
+aiohttp==3.9.3
+aiosignal==1.3.1
+altair==5.3.0
+annotated-types==0.6.0
+anyio==4.3.0
+asgiref==3.8.1
+attrs==23.2.0
+blinker==1.8.1
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.1
+cycler==0.12.1
+datasets==2.18.0
+dill==0.3.8
+Django==5.0.3
+dnspython==2.6.1
+email_validator==2.1.1
+fastapi==0.111.0
+fastapi-cli==0.0.2
+ffmpy==0.3.2
+filelock==3.13.4
+Flask==3.0.3
+fonttools==4.51.0
+frozenlist==1.4.1
+fsspec==2024.2.0
+gitdb==4.0.11
+GitPython==3.1.43
+gradio==4.29.0
+gradio_client==0.16.1
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.22.2
+idna==3.6
+importlib_resources==6.4.0
+itsdangerous==2.2.0
+Jinja2==3.1.3
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.8.4
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.5
+multiprocess==0.70.16
+mysqlclient==2.2.4
+networkx==3.3
+numpy==1.26.4
+orjson==3.10.3
+packaging==24.0
+pandas==2.2.1
+pillow==10.3.0
+protobuf==4.25.3
+pyarrow==15.0.2
+pyarrow-hotfix==0.6
+pydantic==2.7.1
+pydantic_core==2.18.2
+pydeck==0.9.0b1
+pydub==0.25.1
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+pytz==2024.1
+PyYAML==6.0.1
+referencing==0.35.0
+regex==2024.4.16
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+ruff==0.4.3
+safetensors==0.4.3
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.16.0
+smmap==5.0.1
+sniffio==1.3.1
+sqlparse==0.4.4
+starlette==0.37.2
+streamlit==1.33.0
+sympy==1.12
+tenacity==8.2.3
+tokenizers==0.19.1
+toml==0.10.2
+tomlkit==0.12.0
+toolz==0.12.1
+torch==2.2.2
+tornado==6.4
+tqdm==4.66.2
+transformers==4.40.1
+typer==0.12.3
+typing_extensions==4.11.0
+tzdata==2024.1
+ujson==5.9.0
+urllib3==2.2.1
+uvicorn==0.29.0
+watchdog==4.0.0
+watchfiles==0.21.0
+websockets==11.0.3
+Werkzeug==3.0.2
+xxhash==3.4.1
+yarl==1.9.4