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app.py

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+import streamlit as st 
+
+import pandas as pd
+import numpy as np
+import onnxruntime as ort
+from transformers import AutoTokenizer
+from huggingface_hub import hf_hub_download 
+
+
+import os
+
+
+
+# download the model from Hugging Face
+tokenizer = AutoTokenizer.from_pretrained('answerdotai/ModernBERT-large')
+if os.path.exists("model_f16.onnx"):
+    st.write("Model already downloaded.")
+else:
+    st.write("Downloading model...")
+    model_path = hf_hub_download(
+        repo_id="bakhil-aissa/anti_prompt_injection",
+        filename="model_f16.onnx",
+        local_dir_use_symlinks=False,
+    )
+
+st.title("Anti Prompt Injection Detection")
+
+
+# Load the ONNX model
+sess = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+# Define the input form
+def predict ( text ):
+    enc = tokenizer([text], return_tensors="np", truncation=True, max_length=2048)
+    inputs = {"input_ids": enc["input_ids"], "attention_mask": enc["attention_mask"]}
+    logits = sess.run(["logits"], inputs)[0]
+    exp = np.exp(logits)
+    probs = exp / exp.sum(axis=1, keepdims=True)        # shape (1, num_classes)
+    return probs
+
+st.subheader("Enter your text to check for prompt injection:")
+text_input = st.text_area("Text Input", height=200)
+confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)
+if st.button("Check"):
+    if text_input:
+        try:
+            with st.spinner("Processing..."):
+                # Call the predict function
+                probs = predict(text_input)
+            jailbreak_prob = float(probs[0][1])  # index into batch
+            is_jailbreak = jailbreak_prob >= confidence_threshold
+            
+            st.success(f"Is Jailbreak: {is_jailbreak}")
+            st.info(f"Jailbreak Probability: {jailbreak_prob:.4f}")
+        except Exception as e:
+            st.error(f"Error: {str(e)}")
+    else:
+        st.warning("Please enter some text to check.")

requirements.txt

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+fastapi==0.116.1
+huggingface_hub==0.33.5
+numpy==1.21.5
+onnxruntime==1.22.0
+pandas==2.3.1
+pydantic==2.11.7
+streamlit==1.44.1
+transformers==4.53.3