Delete app.py

app.py

@@ -1,75 +0,0 @@
-
-
-import streamlit as st
-import numpy as np
-import pandas as pd
-from tensorflow.keras.preprocessing.text import Tokenizer
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.models import load_model
-from lime.lime_text import LimeTextExplainer
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Load the trained model
-@st.cache_resource
-def load_trained_model():
-    model = load_model("deep_learning_model.h5")
-    return model
-
-model = load_trained_model()
-
-# Tokenizer setup
-tokenizer = Tokenizer(num_words=5000)
-max_length = 100
-
-# Load Data
-@st.cache_data
-def load_data():
-    data = pd.read_csv("train prompt.csv", sep=',', quoting=3, encoding='ISO-8859-1', on_bad_lines='skip', engine='python')
-    data['label'] = data['label'].replace({'valid': 0, 'malicious': 1})
-    return data
-
-data = load_data()
-tokenizer.fit_on_texts(data['input'].values)
-
-# Preprocessing functions
-def preprocess_prompt(prompt, tokenizer, max_length):
-    sequence = tokenizer.texts_to_sequences([prompt])
-    padded_sequence = pad_sequences(sequence, maxlen=max_length)
-    return padded_sequence
-
-def detect_prompt(prompt, model, tokenizer, max_length):
-    processed_prompt = preprocess_prompt(prompt, tokenizer, max_length)
-    prediction = model.predict(processed_prompt)[0][0]
-    class_label = 'Malicious' if prediction >= 0.5 else 'Valid'
-    confidence_score = prediction * 100 if prediction >= 0.5 else (1 - prediction) * 100
-    return class_label, confidence_score
-
-# Load model
-model = load_trained_model()
-
-# Streamlit app
-st.title("Prompt Injection Attack Detection")
-st.write("This application detects malicious prompts to prevent injection attacks.")
-
-prompt = st.text_input("Enter a prompt to analyze:")
-
-if prompt:
-    class_label, confidence = detect_prompt(prompt, model, tokenizer, max_length)
-    st.write(f"### Prediction: {class_label}")
-    st.write(f"Confidence: {confidence:.2f}%")
-
-    # LIME explanation
-    st.write("Generating LIME Explanation...")
-    explainer = LimeTextExplainer(class_names=["Valid", "Malicious"])
-
-    def predict_fn(prompts):
-        sequences = tokenizer.texts_to_sequences(prompts)
-        padded_sequences = pad_sequences(sequences, maxlen=max_length)
-        predictions = model.predict(padded_sequences)
-        return np.hstack([1 - predictions, predictions])
-
-    explanation = explainer.explain_instance(prompt, predict_fn, num_features=10)
-    fig = explanation.as_pyplot_figure()
-    st.pyplot(fig)
-