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import streamlit as st |
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from datasets import load_dataset |
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import os |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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st.set_page_config(page_title="Synthetic textbooks inspection", layout="wide") |
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st.title("Synthetic textbooks inspection") |
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st.markdown("Inspection of synthetic textbooks generated by `Falcon-180B-chat`") |
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@st.cache_data() |
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def load_data(source="all"): |
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ds = load_dataset("HuggingFaceTB/synthetic_textbooks_subset", split="train", use_auth_token=HF_TOKEN) |
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if source != "all": |
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ds = ds.filter(lambda x: x["source"] == source) |
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return ds |
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source = st.selectbox("Data source", ['all', 'wikihow','khan_academy', 'stanford_courses', 'rw_wikihow', 'rw_stanford']) |
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samples = load_data(source) |
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n_samples = len(samples) |
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index = st.number_input(f"Index of the sample (out of {n_samples}):", min_value=0, max_value=n_samples-1, value=0, step=1) |
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st.markdown(f"Displaying source: {source}") |
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st.subheader("Prompt") |
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st.markdown(samples[index_example]["prompt"]) |
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st.subheader("Textbook") |
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st.markdown(samples[index_example]['textbook']) |
