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"""
🇬🇧 Module: comparison_table.py
Purpose: Generates comparative DataFrame for two portfolios and an LLM commentary.
🇷🇺 Модуль: comparison_table.py
Назначение: создаёт сравнительную таблицу метрик двух портфелей и комментарий LLM.
"""
import pandas as pd
import asyncio
from services.output_api import fetch_metrics_async, extract_portfolio_id
from services.llm_client import llm_service
from prompts.system_prompts import COMPARISON_SYSTEM_PROMPT
def show_comparison_table(portfolio_a: str, portfolio_b: str):
"""Public Gradio entry: returns both a DataFrame and LLM commentary."""
pid_a = extract_portfolio_id(portfolio_a)
pid_b = extract_portfolio_id(portfolio_b)
if not pid_a or not pid_b:
return "❌ Invalid portfolio IDs.", "No commentary available."
try:
df, commentary = asyncio.run(_build_comparison_with_comment(pid_a, pid_b))
return df, commentary
except Exception as e:
return f"❌ Error building comparison table: {e}", "❌ LLM analysis failed."
async def _build_comparison_with_comment(p1: str, p2: str):
"""Async helper: builds table and gets commentary."""
m1 = await fetch_metrics_async(p1)
m2 = await fetch_metrics_async(p2)
if not m1 or not m2:
raise ValueError("Metrics unavailable for one or both portfolios.")
all_keys = sorted(set(m1.keys()) | set(m2.keys()))
rows = []
for k in all_keys:
v1 = m1.get(k, 0)
v2 = m2.get(k, 0)
diff = v1 - v2
symbol = "▲" if diff > 0 else "▼" if diff < 0 else "—"
rows.append({
"Metric": k,
"Portfolio A": round(v1, 3),
"Portfolio B": round(v2, 3),
"Δ Difference": f"{symbol} {diff:+.3f}"
})
df = pd.DataFrame(rows, columns=["Metric", "Portfolio A", "Portfolio B", "Δ Difference"])
# Generate LLM commentary
summary = "\n".join(f"{r['Metric']}: {r['Δ Difference']}" for r in rows)
prompt = (
f"{COMPARISON_SYSTEM_PROMPT}\n"
f"Compare and explain the differences between Portfolio A and B:\n{summary}\n"
f"Write your insights as a concise professional commentary."
)
commentary = ""
for delta in llm_service.stream_chat(
messages=[
{"role": "system", "content": "You are an investment portfolio analyst."},
{"role": "user", "content": prompt},
],
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
):
commentary += delta
return df, commentary
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