William Jeynes
148 lines
4.6 KiB
Python
148 lines
4.6 KiB
Python
import streamlit as st
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import json
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import random
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from pathlib import Path
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# Path to your JSONL file
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DATA_FILE = "../../data/results.jsonl"
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# --------------------------
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# Helper functions
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# --------------------------
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def load_data(file_path):
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"""Load JSONL file into a list of dicts with parsed content."""
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data = []
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if Path(file_path).exists():
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with open(file_path, "r", encoding="utf-8") as f:
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for line in f:
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if line.strip():
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entry = json.loads(line)
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for o in entry.get("output", []):
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if "content" in o:
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try:
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o["content_parsed"] = json.loads(o["content"])
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except json.JSONDecodeError:
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o["content_parsed"] = []
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print("parse error")
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data.append(entry)
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return data
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def save_data(file_path, data):
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"""Save the updated data back to JSONL file."""
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with open(file_path, "w", encoding="utf-8") as f:
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for entry in data:
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for o in entry.get("output", []):
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if "content_parsed" in o:
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o["content"] = json.dumps(o["content_parsed"], ensure_ascii=False)
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f.write(json.dumps(entry, ensure_ascii=False) + "\n")
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# --------------------------
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# Initialize session state
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# --------------------------
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if "data" not in st.session_state:
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st.session_state.data = load_data(DATA_FILE)
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if "current_claim" not in st.session_state:
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st.session_state.current_claim = None
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st.title("Claim Visualizer")
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# --------------------------
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# Sidebar
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# --------------------------
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view = st.sidebar.selectbox("Choose View", ["All Claims", "Single Claim Random"])
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# --------------------------
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# All Claims View
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# --------------------------
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if view == "All Claims":
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st.header("All Claims")
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for entry in st.session_state.data:
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st.subheader(f"{entry.get('text')}")
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for o in entry.get("output", []):
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for c in o.get("content_parsed", []):
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st.markdown(f"**Event:** {c.get('event')}")
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st.markdown(f"**Reasoning:** {c.get('reasoningWhyRelevant')}")
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st.markdown(f"**Score:** {c.get('score')}")
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st.markdown(f"**Human Score:** {c.get('human_score')}")
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st.markdown("---")
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# --------------------------
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# Single Claim Random View (Ranking Based)
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# --------------------------
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elif view == "Single Claim Random":
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# Select an entry that still has unscored items
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if st.session_state.current_claim is None:
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unscored_entries = []
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for entry in st.session_state.data:
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unscored = []
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for o in entry.get("output", []):
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for c in o.get("content_parsed", []):
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if c.get("human_score") is None:
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unscored.append(c)
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if unscored:
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unscored_entries.append({
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"entry": entry,
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"claims": unscored
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})
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if unscored_entries:
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st.session_state.current_claim = random.choice(unscored_entries)
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else:
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st.session_state.current_claim = None
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bundle = st.session_state.current_claim
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if bundle is None:
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st.info("No entries remaining without human scores.")
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else:
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entry = bundle["entry"]
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claims = bundle["claims"]
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st.subheader(entry.get("text"))
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st.write("Rank events (1 = best / most relevant)")
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rankings = []
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# Collect rankings
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for i, c in enumerate(claims):
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st.markdown(f"### Event {i+1}")
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st.markdown(f"**Event:** {c.get('event')}")
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st.markdown(f"**Reasoning:** {c.get('reasoningWhyRelevant')}")
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rank = st.number_input(
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f"Rank for event {i+1}",
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min_value=1,
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max_value=len(claims),
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key=f"rank_{i}"
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)
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rankings.append((c, rank))
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if st.button("Submit Ranking"):
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# Sort by rank (ascending = best)
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rankings.sort(key=lambda x: x[1])
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n = len(rankings)
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# Convert ranking -> normalized score
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for idx, (claim_obj, _) in enumerate(rankings):
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if n == 1:
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score = 1.0
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else:
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score = 1 - (idx / (n - 1))
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claim_obj["human_score"] = round(score, 3)
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save_data(DATA_FILE, st.session_state.data)
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st.success("Ranking converted to scores and saved!")
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st.session_state.current_claim = None
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st.rerun() |