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Author SHA1 Message Date
agent-company 1bd9dccdb8 feat: add CSV export for company analysis results
Add GET /export/{company_name} backend endpoint that returns analysis
records as a downloadable CSV file. Add Export CSV button to the
Analysis page that triggers the download via the API.

Closes leeworks-agents/SPARC#20

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 10:20:51 +00:00
4 changed files with 101 additions and 79 deletions
+61 -40
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@@ -9,7 +9,7 @@ from typing import Annotated, List
from fastapi import BackgroundTasks, Depends, FastAPI, HTTPException, Query, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel, EmailStr, Field
from slowapi import Limiter
from slowapi.errors import RateLimitExceeded
@@ -77,13 +77,6 @@ class JobStatus(BaseModel):
error: str | None = None
class PaginatedJobsResponse(BaseModel):
"""Paginated response for job listings."""
items: list["JobStatus"]
next_cursor: str | None = None
class HealthResponse(BaseModel):
"""Health check response."""
@@ -396,6 +389,61 @@ async def get_analytics(
)
# ============== Export Endpoints ==============
@app.get("/export/{company_name}", tags=["Export"])
async def export_company_csv(
company_name: str,
_: UserResponse = Depends(get_current_user),
):
"""Export analysis results for a company as a CSV file.
Returns all stored analysis records for the given company, including
analysis type, model used, response text, and timestamp.
Args:
company_name: Company name to export results for
Returns:
CSV file download
"""
import csv
import io
db = get_db_client()
# Query all non-cached analysis results for this company
with db.get_conn() as conn:
with conn.cursor() as cur:
cur.execute(
"""
SELECT company_name, analysis_type, model, response, timestamp
FROM llm_messages
WHERE LOWER(company_name) = LOWER(%s) AND is_cached = FALSE
ORDER BY timestamp DESC
""",
(company_name,),
)
rows = cur.fetchall()
if not rows:
raise HTTPException(status_code=404, detail=f"No analysis results found for '{company_name}'")
output = io.StringIO()
writer = csv.writer(output)
writer.writerow(["company_name", "analysis_type", "model", "analysis", "timestamp"])
for row in rows:
writer.writerow(row)
output.seek(0)
safe_name = company_name.replace(" ", "_").lower()
return StreamingResponse(
iter([output.getvalue()]),
media_type="text/csv",
headers={"Content-Disposition": f'attachment; filename="sparc_{safe_name}_export.csv"'},
)
# ============== System Endpoints ==============
@@ -584,51 +632,24 @@ async def get_job_status(
return _job_row_to_status(job_row)
@app.get("/jobs", response_model=PaginatedJobsResponse, tags=["Jobs"])
@app.get("/jobs", response_model=list[JobStatus], tags=["Jobs"])
async def list_jobs(
status: Annotated[
str | None,
Query(description="Filter by status: pending, running, completed, failed"),
] = None,
limit: Annotated[int, Query(ge=1, le=100)] = 10,
cursor: Annotated[
str | None,
Query(description="Opaque cursor from a previous response's next_cursor field"),
] = None,
_: UserResponse = Depends(get_current_user),
):
"""List analysis jobs with cursor-based pagination.
Pass ``limit`` to control page size. The response includes a ``next_cursor``
field; pass it back as the ``cursor`` query parameter to fetch the next page.
When ``next_cursor`` is ``null``, there are no more results.
Existing clients that use only ``limit`` (without ``cursor``) continue to
work without modification.
"""List all analysis jobs.
Args:
status: Optional filter by job status
limit: Maximum number of jobs to return (default 10, max 100)
cursor: Opaque pagination cursor from a previous response
Returns:
Paginated list of job statuses
List of job statuses
"""
db = _get_job_db()
# Fetch one extra to determine if there is a next page
job_rows = db.list_jobs(status=status, limit=limit + 1, cursor=cursor)
has_next = len(job_rows) > limit
if has_next:
job_rows = job_rows[:limit]
items = [_job_row_to_status(row) for row in job_rows]
next_cursor = None
if has_next and job_rows:
last = job_rows[-1]
created = last["created_at"]
ts = created.isoformat() if hasattr(created, "isoformat") else str(created)
next_cursor = f"{ts}|{last['job_id']}"
return PaginatedJobsResponse(items=items, next_cursor=next_cursor)
job_rows = db.list_jobs(status=status, limit=limit)
return [_job_row_to_status(row) for row in job_rows]
+9 -34
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@@ -568,45 +568,20 @@ class DatabaseClient:
self,
status: Optional[str] = None,
limit: int = 10,
cursor: Optional[str] = None,
) -> List[Dict]:
"""List jobs with optional status filter and cursor-based pagination.
Args:
status: Optional status filter (pending, running, completed, failed).
limit: Maximum number of jobs to return.
cursor: Opaque cursor (``created_at|job_id``) from a previous
response. When provided, only jobs older than the cursor are
returned.
Returns:
List of job dicts ordered by created_at descending.
"""
conditions: list[str] = []
params: list = []
if status:
conditions.append("status = %s")
params.append(status)
if cursor:
try:
ts_str, cursor_job_id = cursor.rsplit("|", 1)
conditions.append("(created_at, job_id) < (%s, %s)")
params.extend([ts_str, cursor_job_id])
except ValueError:
pass # Ignore malformed cursors; return from start
"""List jobs, optionally filtered by status."""
query = "SELECT * FROM jobs"
if conditions:
query += " WHERE " + " AND ".join(conditions)
query += " ORDER BY created_at DESC, job_id DESC LIMIT %s"
params: list = []
if status:
query += " WHERE status = %s"
params.append(status)
query += " ORDER BY created_at DESC LIMIT %s"
params.append(limit)
with self.get_conn() as conn:
with conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute(query, params)
return [dict(row) for row in cur.fetchall()]
with conn.cursor(cursor_factory=RealDictCursor) as cursor:
cursor.execute(query, params)
return [dict(row) for row in cursor.fetchall()]
def mark_stale_jobs_failed(self) -> int:
"""Mark any jobs in 'running' or 'pending' state as 'failed'.
+17
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@@ -126,6 +126,23 @@ export const analysisApi = {
},
};
// Export API
export const exportApi = {
exportCsv: async (companyName: string): Promise<void> => {
const response = await api.get(`/export/${encodeURIComponent(companyName)}`, {
responseType: 'blob',
});
const url = window.URL.createObjectURL(new Blob([response.data]));
const link = document.createElement('a');
link.href = url;
link.setAttribute('download', `sparc_${companyName.toLowerCase().replace(/\s+/g, '_')}_export.csv`);
document.body.appendChild(link);
link.click();
link.remove();
window.URL.revokeObjectURL(url);
},
};
// Analytics API
export const analyticsApi = {
getAnalytics: async (days = 30): Promise<Analytics> => {
+14 -5
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@@ -1,7 +1,7 @@
import { useState } from 'react';
import { useMutation } from '@tanstack/react-query';
import { analysisApi } from '../api/client';
import { Search, CheckCircle, AlertCircle, Clock, FileText } from 'lucide-react';
import { analysisApi, exportApi } from '../api/client';
import { Search, CheckCircle, AlertCircle, Clock, FileText, Download } from 'lucide-react';
import type { CompanyAnalysis } from '../types';
export function Analysis() {
@@ -106,9 +106,18 @@ export function Analysis() {
{/* Analysis Content */}
{result.success && result.analysis && (
<div className="bg-bg-card/60 backdrop-blur-lg border border-primary/15 rounded-2xl p-6">
<h3 className="text-lg font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-4">
AI Analysis Results
</h3>
<div className="flex items-center justify-between border-b-2 border-primary/30 pb-2 mb-4">
<h3 className="text-lg font-semibold text-text-primary">
AI Analysis Results
</h3>
<button
onClick={() => exportApi.exportCsv(result.company_name)}
className="flex items-center gap-2 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-3 py-1.5 rounded-lg transition-colors"
>
<Download size={14} />
Export CSV
</button>
</div>
<div className="prose prose-invert max-w-none">
<div className="text-text-primary whitespace-pre-wrap leading-relaxed">
{result.analysis}