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from __future__ import annotations
import uuid
from typing import Optional
from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, Query, UploadFile
from app.core.config import settings
from app.core.logging import get_logger
from app.core.security import get_api_key
from app.models.schemas import (
AnalysisResult,
AnalysisStatus,
ComparisonRequest,
ComparisonResult,
FilterParams,
JobStatus,
TopicInfo,
)
from app.services.analysis_pipeline import (
filter_entries,
get_all_jobs,
get_job,
run_analysis,
)
from app.services.file_processing import parse_file
logger = get_logger(__name__)
router = APIRouter(prefix="/api/v1", tags=["analysis"])
@router.post("/upload", response_model=JobStatus)
async def upload_file(
background_tasks: BackgroundTasks,
file: UploadFile = File(...),
source: Optional[str] = Query(None, description="Data source label"),
api_key: str = Depends(get_api_key),
):
"""Upload a file for analysis. Supports CSV, JSON, Excel, ZIP."""
if not file.filename:
raise HTTPException(status_code=400, detail="No filename provided")
content = await file.read()
size_mb = len(content) / (1024 * 1024)
if size_mb > settings.max_upload_size_mb:
raise HTTPException(
status_code=413,
detail=f"File too large ({size_mb:.1f}MB). Maximum: {settings.max_upload_size_mb}MB",
)
try:
entries = parse_file(content, file.filename, source)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc))
if not entries:
raise HTTPException(status_code=400, detail="No valid entries found in the uploaded file")
job_id = uuid.uuid4().hex[:12]
logger.info("upload_received", job_id=job_id, filename=file.filename, entries=len(entries), size_mb=round(size_mb, 2))
background_tasks.add_task(run_analysis, entries, job_id)
from datetime import datetime
return JobStatus(
job_id=job_id,
status=AnalysisStatus.PENDING,
progress=0.0,
message=f"Processing {len(entries)} entries from {file.filename}",
created_at=datetime.utcnow(),
)
@router.post("/upload/chunked", response_model=JobStatus)
async def upload_chunked(
background_tasks: BackgroundTasks,
file: UploadFile = File(...),
chunk_index: int = Query(0, ge=0),
total_chunks: int = Query(1, ge=1),
upload_id: Optional[str] = Query(None),
source: Optional[str] = Query(None),
api_key: str = Depends(get_api_key),
):
"""Chunked upload for files >10MB."""
from pathlib import Path
upload_id = upload_id or uuid.uuid4().hex[:12]
chunk_dir = settings.upload_path / f"chunks_{upload_id}"
chunk_dir.mkdir(parents=True, exist_ok=True)
content = await file.read()
chunk_path = chunk_dir / f"chunk_{chunk_index:04d}"
chunk_path.write_bytes(content)
logger.info("chunk_received", upload_id=upload_id, chunk=chunk_index, total=total_chunks)
if chunk_index + 1 < total_chunks:
from datetime import datetime
return JobStatus(
job_id=upload_id,
status=AnalysisStatus.PENDING,
progress=chunk_index / total_chunks,
message=f"Received chunk {chunk_index + 1}/{total_chunks}",
created_at=datetime.utcnow(),
)
# All chunks received — reassemble
chunks = sorted(chunk_dir.glob("chunk_*"))
combined = b"".join(c.read_bytes() for c in chunks)
# Clean up chunks
for c in chunks:
c.unlink()
chunk_dir.rmdir()
try:
filename = file.filename or "upload.csv"
entries = parse_file(combined, filename, source)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc))
if not entries:
raise HTTPException(status_code=400, detail="No valid entries found")
background_tasks.add_task(run_analysis, entries, upload_id)
from datetime import datetime
return JobStatus(
job_id=upload_id,
status=AnalysisStatus.PROCESSING,
progress=0.0,
message=f"All chunks received. Processing {len(entries)} entries.",
created_at=datetime.utcnow(),
)
@router.get("/jobs", response_model=list[JobStatus])
async def list_jobs(api_key: str = Depends(get_api_key)):
"""List all analysis jobs."""
jobs = get_all_jobs()
return [
JobStatus(
job_id=j.job_id,
status=j.status,
progress=1.0 if j.status == AnalysisStatus.COMPLETED else 0.5,
message="",
created_at=j.created_at,
completed_at=j.completed_at,
)
for j in jobs
]
@router.get("/jobs/{job_id}", response_model=AnalysisResult)
async def get_job_result(job_id: str, api_key: str = Depends(get_api_key)):
"""Get analysis results for a specific job."""
job = get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
return job
@router.get("/jobs/{job_id}/status", response_model=JobStatus)
async def get_job_status(job_id: str, api_key: str = Depends(get_api_key)):
"""Get status of an analysis job."""
job = get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
return JobStatus(
job_id=job.job_id,
status=job.status,
progress=1.0 if job.status == AnalysisStatus.COMPLETED else 0.5,
message="",
created_at=job.created_at,
completed_at=job.completed_at,
)
@router.post("/jobs/{job_id}/filter")
async def filter_job_results(
job_id: str,
filters: FilterParams,
api_key: str = Depends(get_api_key),
):
"""Filter analysis results."""
job = get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
if job.status != AnalysisStatus.COMPLETED:
raise HTTPException(status_code=400, detail="Analysis not yet completed")
filtered = filter_entries(
job.entries,
date_from=filters.date_from,
date_to=filters.date_to,
sentiment_min=filters.sentiment_min,
sentiment_max=filters.sentiment_max,
topics=filters.topics,
languages=filters.languages,
sources=filters.sources,
search_text=filters.search_text,
)
# Paginate
start = (filters.page - 1) * filters.page_size
end = start + filters.page_size
return {
"total": len(filtered),
"page": filters.page,
"page_size": filters.page_size,
"entries": filtered[start:end],
}
@router.post("/jobs/{job_id}/compare", response_model=ComparisonResult)
async def compare_segments(
job_id: str,
comparison: ComparisonRequest,
api_key: str = Depends(get_api_key),
):
"""Compare two data segments from the same job."""
job = get_job(job_id)
if not job:
raise HTTPException(status_code=404, detail=f"Job {job_id} not found")
if job.status != AnalysisStatus.COMPLETED:
raise HTTPException(status_code=400, detail="Analysis not yet completed")
from collections import Counter
import numpy as np
from app.models.schemas import AnalysisSummary, SentimentLabel
seg_a_entries = filter_entries(
job.entries, **comparison.segment_a.model_dump(exclude={"page", "page_size"})
)
seg_b_entries = filter_entries(
job.entries, **comparison.segment_b.model_dump(exclude={"page", "page_size"})
)
def make_summary(entries):
if not entries:
return AnalysisSummary(
total_entries=0, avg_sentiment=0.5,
dominant_sentiment=SentimentLabel.NEUTRAL,
num_topics=0, top_topics=[], languages_detected=[],
)
sentiments = [e.sentiment for e in entries]
topic_counts = Counter(e.topic_id for e in entries)
return AnalysisSummary(
total_entries=len(entries),
avg_sentiment=round(float(np.mean([s.score for s in sentiments])), 4),
dominant_sentiment=SentimentLabel(
Counter(s.label.value for s in sentiments).most_common(1)[0][0]
),
num_topics=len(set(e.topic_id for e in entries) - {-1}),
top_topics=[
TopicInfo(topic_id=tid, label=f"Topic {tid}", keywords=[], size=cnt)
for tid, cnt in topic_counts.most_common(5) if tid != -1
],
languages_detected=list(set(e.language.language for e in entries)),
)
sum_a = make_summary(seg_a_entries)
sum_b = make_summary(seg_b_entries)
topics_a = set(e.topic_id for e in seg_a_entries) - {-1}
topics_b = set(e.topic_id for e in seg_b_entries) - {-1}
return ComparisonResult(
segment_a=sum_a,
segment_b=sum_b,
sentiment_delta=round(sum_b.avg_sentiment - sum_a.avg_sentiment, 4),
topic_changes=[],
new_topics=[
TopicInfo(topic_id=t, label=f"Topic {t}", keywords=[], size=0)
for t in topics_b - topics_a
],
disappeared_topics=[
TopicInfo(topic_id=t, label=f"Topic {t}", keywords=[], size=0)
for t in topics_a - topics_b
],
)
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