Delete app.py

app.py

@@ -1,68 +0,0 @@
-
-
-import streamlit as st
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
-
-# Constants
-MAX_LENGTH = 100
-TOKENIZER_PATH = "tokenizer.json"  # Assuming the tokenizer is saved separately.
-
-# Load pre-trained model
-@st.cache_resource
-def load_trained_model():
-    return load_model("deep_learning_model.h5")
-
-# Load tokenizer
-@st.cache_resource
-def load_tokenizer():
-    import json
-    from tensorflow.keras.preprocessing.text import tokenizer_from_json
-    with open(TOKENIZER_PATH, "r") as f:
-        tokenizer_data = json.load(f)
-    return tokenizer_from_json(tokenizer_data)
-
-# Preprocessing function
-def preprocess_prompt(prompt, tokenizer, max_length):
-    sequence = tokenizer.texts_to_sequences([prompt])
-    padded_sequence = pad_sequences(sequence, maxlen=max_length)
-    return padded_sequence
-
-# Predict function
-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
-
-# Streamlit App
-st.title("Prompt Injection Detection App")
-st.write("Detect and prevent prompt injection attacks using a deep learning model.")
-
-# Load model and tokenizer
-model = load_trained_model()
-tokenizer = load_tokenizer()
-
-# Input Section
-user_input = st.text_area("Enter a prompt to test:", "")
-if st.button("Detect"):
-    if user_input:
-        label, confidence = detect_prompt(user_input, model, tokenizer, MAX_LENGTH)
-        st.write(f"**Predicted Class:** {label}")
-        st.write(f"**Confidence Score:** {confidence:.2f}%")
-    else:
-        st.warning("Please enter a prompt to test.")
-        
-import os
-
-if st.button("Train Model"):
-    os.system("python train_model.py")
-    st.success("Model training complete. Saved as deep_learning_model.h5")
-
-if not os.path.exists("deep_learning_model.h5"):
-    st.info("Training the model for the first time...")
-    os.system("python train_model.py")
-    st.success("Model trained successfully and saved as deep_learning_model.h5")