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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
import requests
from PIL import Image
from io import BytesIO
# Define the template
TEMPLATE = """[INST] Evaluate if the given text is harmful i.e. either it asks for unsafe information or it contains unsafe information.
<START TEXT>
{prompt}
<END TEXT>
Answer: [/INST]
"""
# Load the model and tokenizer
@st.cache_data(persist="disk")
def load_model():
model_name = "walledai/walledguard-c"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return tokenizer, model
# Function to load image from URL
@st.cache_data()
def load_image_from_url(url):
response = requests.get(url)
img = Image.open(BytesIO(response.content))
return img
# Evaluation fragment
@st.experimental_fragment
def evaluate_text(user_input, result_container):
if user_input:
# Load model and tokenizer
tokenizer, model = load_model()
# Prepare input
input_ids = tokenizer.encode(TEMPLATE.format(prompt=user_input), return_tensors="pt")
# Generate output
output = model.generate(input_ids=input_ids, max_new_tokens=20, pad_token_id=0)
# Decode output
prompt_len = input_ids.shape[-1]
output_decoded = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
# Determine prediction
prediction = 'unsafe' if 'unsafe' in output_decoded.lower() else 'safe'
# Display results
with result_container:
st.subheader("Evaluation Result:")
st.write(f"The text is evaluated as: **{prediction.upper()}**")
else:
with result_container:
st.warning("Please enter some text to evaluate.")
# Streamlit app
st.title("Text Safety Evaluator")
# User input
user_input = st.text_area("Enter the text you want to evaluate:", height=100)
# Create an empty container for the result
result_container = st.empty()
if st.button("Evaluate"):
evaluate_text(user_input, result_container)
# Logo fragment
@st.experimental_fragment
def display_logo(logo_container):
logo_url = "https://github.com/walledai/walledeval/assets/32847115/d8b1d14f-7071-448b-8997-2eeba4c2c8f6"
logo = load_image_from_url(logo_url)
with logo_container:
st.image(logo, use_column_width=True, width=500) # Adjust the width as needed
# Info fragment
@st.experimental_fragment
def display_info(info_container):
with info_container:
st.info("For a more performant version, check out Walled Guard Advanced. Connect with us at admin@walled.ai for more information.")
# Add logo at the bottom center
col1, col2, col3 = st.columns([1,2,1])
logo_container = col2.empty()
display_logo(logo_container)
# Add information about Walled Guard Advanced
col1, col2, col3 = st.columns([1,2,1])
info_container = col2.empty()
display_info(info_container) |