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Kovács Ádám
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Parent(s):
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Add code to spaces
Browse files- app.py +92 -0
- requirements.txt +2 -0
app.py
ADDED
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import streamlit as st
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from lettucedetect.models.inference import HallucinationDetector
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import streamlit.components.v1 as components
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def create_interactive_text(text: str, spans: list[dict[str, int | float]]) -> str:
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"""Create interactive HTML with highlighting and hover effects.
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:param text: The text to create the interactive text for.
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:param spans: The spans to highlight.
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:return: The interactive text.
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"""
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html_text = text
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for span in sorted(spans, key=lambda x: x["start"], reverse=True):
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span_text = text[span["start"] : span["end"]]
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highlighted_span = f'<span class="hallucination" title="Confidence: {span["confidence"]:.3f}">{span_text}</span>'
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html_text = (
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html_text[: span["start"]] + highlighted_span + html_text[span["end"] :]
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)
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return f"""
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<style>
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.container {{
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font-family: Arial, sans-serif;
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font-size: 16px;
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line-height: 1.6;
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padding: 20px;
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}}
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.hallucination {{
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background-color: rgba(255, 99, 71, 0.3);
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padding: 2px;
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border-radius: 3px;
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cursor: help;
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}}
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.hallucination:hover {{
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background-color: rgba(255, 99, 71, 0.5);
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}}
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</style>
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<div class="container">{html_text}</div>
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"""
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def main():
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st.set_page_config(page_title="Lettuce Detective")
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st.image(
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"https://github.com/KRLabsOrg/LettuceDetect/blob/main/assets/lettuce_detective.png?raw=true",
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width=600,
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)
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st.title("Let Us Detect Your Hallucinations")
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@st.cache_resource
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def load_detector():
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return HallucinationDetector(
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method="transformer",
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model_path="KRLabsOrg/lettucedect-base-modernbert-en-v1",
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)
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detector = load_detector()
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context = st.text_area(
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"Context",
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"France is a country in Europe. The capital of France is Paris. The population of France is 67 million.",
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height=100,
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)
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question = st.text_area(
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"Question",
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"What is the capital of France? What is the population of France?",
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height=100,
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)
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answer = st.text_area(
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"Answer",
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"The capital of France is Paris. The population of France is 69 million.",
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height=100,
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)
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if st.button("Detect Hallucinations"):
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predictions = detector.predict(
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context=[context], question=question, answer=answer, output_format="spans"
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)
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html_content = create_interactive_text(answer, predictions)
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components.html(html_content, height=200)
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if __name__ == "__main__":
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main()
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requirements.txt
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@@ -0,0 +1,2 @@
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lettucedetect
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streamlit
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