Create app.py

app.py

@@ -0,0 +1,39 @@
+import streamlit as st
+from datasets import load_dataset
+import os
+
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+st.set_page_config(
+    page_title="Logprobs inspection", layout="wide"
+)
+
+st.markdown("# Logprobs inspection")
+
+@st.cache_data
+def load_data():
+    ds = load_dataset(
+        "HuggingFaceTB/sample_log_probs",
+        split="train",
+        token=HF_TOKEN,
+    )
+    return ds
+
+ds = load_data()
+
+min_log = min(ds["logprobs"])
+max_log = max(ds["logprobs"])
+col_1, col_2 = st.columns(2)
+with col_1:
+    min_score = st.slider("Select minimum logprob", min_value=min_log, max_value=max_log, value=min_log, step=0.2, key="min_score")
+with col_2:
+    max_score = st.slider("Select maximum logprob", min_value=min_log, max_value=max_log, value=max_log, step=0.2, key="max_score")
+
+filtered_ds = ds.filter(lambda x: min_score <= x["logprobs"] <= max_score)
+index = st.slider("Select a sample", 0, len(filtered_ds), 0)
+
+with st.expander("The prompt"):
+    st.markdown(filtered_ds[index]['prompt'])
+
+st.markdown(f"**Metadata:** log_prob is {filtered_ds[index]['logprobs']:.2f}, seed: {filtered_ds[index]['seed_data']}, {filtered_ds[index]['format']} for {filtered_ds[index]['audience']}.")
+st.markdown(filtered_ds[index]["text"])