NeonClary
LLM Comparison Tool: deploy snapshot for Hugging Face Space (orphan history)
08b0543
from __future__ import annotations
import csv
import io
import re
from datetime import datetime
from typing import Any
from fastapi import APIRouter, File, Form, HTTPException, UploadFile
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
from app.services.comparison import run_comparison, run_csv_comparison, stream_comparison
router = APIRouter()
class CompareRequest(BaseModel):
query: str
neon_selections: list[dict[str, str]]
comparison_model_ids: list[str] = []
session_id: str | None = None
persona_target: str = "neon-only"
@router.post("/compare")
async def compare(req: CompareRequest):
if not req.neon_selections:
raise HTTPException(status_code=400, detail="Select at least one Neon model")
groups = await run_comparison(
query=req.query,
neon_selections=req.neon_selections,
comparison_model_ids=req.comparison_model_ids,
session_id=req.session_id,
persona_target=req.persona_target,
)
return {"groups": groups}
@router.post("/compare/stream")
async def compare_stream(req: CompareRequest):
if not req.neon_selections:
raise HTTPException(status_code=400, detail="Select at least one Neon model")
return StreamingResponse(
stream_comparison(
query=req.query,
neon_selections=req.neon_selections,
comparison_model_ids=req.comparison_model_ids,
session_id=req.session_id,
persona_target=req.persona_target,
),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"X-Accel-Buffering": "no",
},
)
def _strip_markdown(text: str) -> str:
"""Remove common markdown formatting from text for CSV output."""
text = re.sub(r'#{1,6}\s+', '', text)
text = re.sub(r'\*\*\*(.+?)\*\*\*', r'\1', text)
text = re.sub(r'\*\*(.+?)\*\*', r'\1', text)
text = re.sub(r'\*(.+?)\*', r'\1', text)
text = re.sub(r'__(.+?)__', r'\1', text)
text = re.sub(r'_(.+?)_', r'\1', text)
text = re.sub(r'~~(.+?)~~', r'\1', text)
text = re.sub(r'`{1,3}(.+?)`{1,3}', r'\1', text, flags=re.DOTALL)
text = re.sub(r'^\s*[-*+]\s+', '', text, flags=re.MULTILINE)
text = re.sub(r'^\s*\d+\.\s+', '', text, flags=re.MULTILINE)
text = re.sub(r'\[([^\]]+)\]\([^)]+\)', r'\1', text)
return text.strip()
@router.post("/compare/csv")
async def compare_csv(
file: UploadFile = File(...),
neon_selections: str = Form(...),
comparison_model_ids: str = Form(""),
persona_target: str = Form("neon-only"),
):
import json
try:
neon_sel = json.loads(neon_selections)
except json.JSONDecodeError:
raise HTTPException(status_code=400, detail="Invalid neon_selections JSON")
comp_ids = []
if comparison_model_ids:
try:
comp_ids = json.loads(comparison_model_ids)
except json.JSONDecodeError:
comp_ids = [s.strip() for s in comparison_model_ids.split(",") if s.strip()]
content = await file.read()
text = content.decode("utf-8-sig")
reader = csv.reader(io.StringIO(text))
questions = []
for row in reader:
if row and row[0].strip():
q = row[0].strip()
if q.lower() not in ("question", "questions", "prompt", "prompts"):
questions.append(q)
if not questions:
raise HTTPException(status_code=400, detail="CSV contains no questions")
all_results = await run_csv_comparison(
questions=questions,
neon_selections=neon_sel,
comparison_model_ids=comp_ids,
persona_target=persona_target,
)
model_keys = _collect_model_keys(all_results)
output = io.StringIO()
output.write('\ufeff')
writer = csv.writer(output)
writer.writerow(["Question"] + model_keys)
for question, groups in zip(questions, all_results):
row_data: dict[str, str] = {}
for group in groups:
persona = group.get("neon_persona", "")
for r in group["responses"]:
key = _response_key(r, persona)
row_data[key] = _strip_markdown(r["response"])
writer.writerow([question] + [row_data.get(k, "") for k in model_keys])
original_name = file.filename or "upload"
stem = original_name.rsplit(".", 1)[0][:12]
now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
filename = f"{stem} LLM Comparison {now}.csv"
output.seek(0)
return StreamingResponse(
iter([output.getvalue().encode("utf-8")]),
media_type="text/csv; charset=utf-8",
headers={"Content-Disposition": f"attachment; filename={filename}"},
)
def _response_key(r: dict[str, Any], neon_persona: str = "") -> str:
if r.get("is_neon"):
return f"Neon / {r['model_name']} ({r.get('persona_name', '')})"
label = f"{r.get('provider_name', r['provider'])} / {r['model_name']}"
if neon_persona:
label += f" - {neon_persona}"
return label
def _collect_model_keys(all_results: list) -> list[str]:
keys: list[str] = []
seen: set[str] = set()
for groups in all_results:
for group in groups:
persona = group.get("neon_persona", "")
for r in group["responses"]:
k = _response_key(r, persona)
if k not in seen:
keys.append(k)
seen.add(k)
return keys