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"""
PDF Processing Routes
Handles Nepali PDF uploads and processing for bias detection
"""
from fastapi import APIRouter, HTTPException, UploadFile, File, Form, Depends
from api.core.deps import get_current_user
from api.schemas import (
PDFProcessingResponse,
PDFToBiasDetectionRequest,
PDFToBiasDetectionResponse,
BiasResult,
)
from typing import List, Optional
import logging
from utility.pdf_processor import PDFProcessor
from .bias_detection import run_bias_detection
logger = logging.getLogger(__name__)
router = APIRouter()
# Initialize PDF Processor
pdf_processor = PDFProcessor()
@router.post("/process-pdf", response_model=PDFProcessingResponse)
async def process_pdf(
file: UploadFile = File(...),
refine_with_llm: bool = Form(default=True),
user: dict = Depends(get_current_user)
):
"""
Upload a Nepali PDF and extract sentences.
- **file**: PDF file to process (required)
- **refine_with_llm**: Whether to refine sentences using Mistral LLM (default: True)
Returns:
- Extracted sentences as a list
- Total number of sentences
- Raw extracted text (optional)
"""
try:
if not file.filename.endswith('.pdf'):
raise HTTPException(
status_code=400,
detail="Only PDF files are supported"
)
logger.info(f"Processing PDF: {file.filename}")
# Read file contents
contents = await file.read()
if not contents:
raise HTTPException(
status_code=400,
detail="Empty file provided"
)
# Process PDF
result = pdf_processor.process_pdf_from_bytes(
pdf_bytes=contents,
refine_with_llm=refine_with_llm
)
if not result["success"]:
raise HTTPException(
status_code=400,
detail=result["error"]
)
logger.info(f"Successfully processed {file.filename}: {result['total_sentences']} sentences")
return PDFProcessingResponse(
success=True,
sentences=result["sentences"],
total_sentences=result["total_sentences"],
raw_text=result["raw_text"],
filename=file.filename
)
except HTTPException:
raise
except Exception as e:
logger.error(f"PDF processing error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@router.post("/process-pdf-to-bias", response_model=PDFToBiasDetectionResponse)
async def process_pdf_to_bias(
file: UploadFile = File(...),
refine_with_llm: bool = Form(default=True),
confidence_threshold: float = Form(default=0.7),
user: dict = Depends(get_current_user)
):
"""
Upload a Nepali PDF, extract sentences, and directly analyze for bias.
- **file**: PDF file to process (required)
- **refine_with_llm**: Whether to refine sentences using Mistral LLM (default: True)
- **confidence_threshold**: Confidence threshold for bias detection (default: 0.7)
Returns:
- Bias detection results for all extracted sentences
- Summary statistics (biased_count, neutral_count)
"""
try:
if not file.filename.endswith('.pdf'):
raise HTTPException(
status_code=400,
detail="Only PDF files are supported"
)
logger.info(f"Processing PDF for bias detection: {file.filename}")
# Read file contents
contents = await file.read()
if not contents:
raise HTTPException(
status_code=400,
detail="Empty file provided"
)
# Step 1: Process PDF
pdf_result = pdf_processor.process_pdf_from_bytes(
pdf_bytes=contents,
refine_with_llm=refine_with_llm
)
if not pdf_result["success"]:
raise HTTPException(
status_code=400,
detail=pdf_result["error"]
)
sentences = pdf_result["sentences"]
logger.info(f"Extracted {len(sentences)} sentences from {file.filename}")
# Step 2: Analyze bias for extracted sentences
combined_text = " ".join(sentences)
bias_result = run_bias_detection(combined_text, confidence_threshold)
logger.info(f"Bias detection completed: {bias_result.biased_count} biased, {bias_result.neutral_count} neutral")
return PDFToBiasDetectionResponse(
success=True,
total_sentences=bias_result.total_sentences,
biased_count=bias_result.biased_count,
neutral_count=bias_result.neutral_count,
results=bias_result.results,
filename=file.filename
)
except HTTPException:
raise
except Exception as e:
logger.error(f"PDF to bias detection error: {e}")
raise HTTPException(status_code=500, detail=str(e))
@router.get("/pdf-health")
async def pdf_processor_health():
"""
Check if the PDF processing service is running properly.
"""
try:
# Test if Mistral client is initialized
llm_available = pdf_processor.llm_client.client is not None
return {
"status": "healthy" if llm_available else "degraded",
"pdf_processor": "ready",
"mistral_client": "connected" if llm_available else "disconnected",
"features": {
"pdf_extraction": True,
"sentence_segmentation": True,
"llm_refinement": llm_available
}
}
except Exception as e:
logger.error(f"Health check failed: {e}")
return {
"status": "unhealthy",
"error": str(e)
}
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