Add application file

app.py

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+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import nltk
+from nltk.tokenize import sent_tokenize
+
+# Set page config at the very beginning
+st.set_page_config(page_title="LLM Detector", layout="centered")
+
+
+# Download the punkt tokenizer for sentence splitting (with caching)
+@st.cache_resource
+def download_nltk_punkt():
+    nltk.download("punkt", quiet=True)
+
+
+download_nltk_punkt()
+
+
+# Load the model and tokenizer (with caching)
+@st.cache_resource
+def load_model_and_tokenizer():
+    model_name = "CoolSpring/creative-writing-llm-detector-deberta-v3-xsmall"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    return tokenizer, model
+
+
+tokenizer, model = load_model_and_tokenizer()
+
+
+def classify_text(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probabilities = torch.softmax(logits, dim=1)
+    return probabilities[0][1].item()  # Probability of being AI-generated
+
+
+def highlight_suspicious_sentences(text):
+    sentences = sent_tokenize(text)
+    scores = [classify_text(sentence) for sentence in sentences]
+    return sentences, scores
+
+
+def get_color(score):
+    if score < 0.33:
+        return "rgba(144, 238, 144, 0.3)"  # Light green
+    elif score < 0.66:
+        return "rgba(255, 255, 0, 0.3)"  # Light yellow
+    else:
+        return "rgba(255, 99, 71, 0.3)"  # Light red
+
+
+st.title("🤖 LLM Detector")
+st.write("Enter text to detect if it's written by an AI language model.")
+
+# Use session state to store the input text
+if "text_input" not in st.session_state:
+    st.session_state.text_input = ""
+
+text_input = st.text_area(
+    "Enter your text here:", value=st.session_state.text_input, height=200
+)
+
+# Update session state when input changes
+if text_input != st.session_state.text_input:
+    st.session_state.text_input = text_input
+
+if st.button("Analyze and Highlight"):
+    if text_input:
+        overall_probability = classify_text(text_input)
+        st.markdown(
+            f"<h3>Overall probability of being AI-generated: <span style='color: {'red' if overall_probability > 0.5 else 'green'};'>{overall_probability:.2%}</span></h3>",
+            unsafe_allow_html=True,
+        )
+
+        st.markdown("### Sentence-level analysis:")
+        sentences, scores = highlight_suspicious_sentences(text_input)
+
+        for sentence, score in zip(sentences, scores):
+            color = get_color(score)
+            st.markdown(
+                f"<div style='background-color: {color}; padding: 10px; margin: 5px 0; border-radius: 5px;'><strong>{score:.2%}</strong> - {sentence}</div>",
+                unsafe_allow_html=True,
+            )
+    else:
+        st.warning("Please enter some text to analyze.")
+
+how_it_works_text = """This LLM Detector uses [CoolSpring/creative-writing-llm-detector-deberta-v3-xsmall](https://huggingface.co/CoolSpring/creative-writing-llm-detector-deberta-v3-xsmall), a DeBERTa-v3-xsmall model fine-tuned for text classification.
+
+It analyzes the input text and estimates the probability of it being generated by an AI language model.
+
+The sentence-level analysis breaks down the input into individual sentences and analyzes each one separately, allowing you to see which parts of the text are more likely to be AI-generated.
+
+Please note that this is not 100% accurate and should be used as a guide rather than a definitive measure."""
+
+if st.button("Fill with Sample Text"):
+    st.session_state.text_input = "\n".join(how_it_works_text.splitlines()[2:])
+    st.rerun()
+
+st.markdown(
+    f"""### How it works
+{how_it_works_text}"""
+)

requirements.txt

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+transformers
+torch
+nltk