api.py

import os
import json
import asyncio
import requests
import fitz  
import shutil
import tempfile
from datetime import datetime
from fastapi import FastAPI, UploadFile, File, HTTPException
import config
import utils_geometry as utils
from engine_vision import process_page_smart
from engine_mapping import map_fields_to_schema
from utils_grouping import group_fields_by_section 

app = FastAPI(title="Smart Contract Processor API")

# code just to create a new commit
def get_fields_from_local_api(pdf_path):
    """
    Sends the PDF to the local model_api to get Bounding Boxes i.e neon green boxes surrounding the fields.
    Identical logic to main.py, just adapted to take a specific path.
    """
    print(f"Sending to Model API: {config.COMMON_FORMS_API_URL}")
    fields_by_page = {}
    try:
        with open(pdf_path, 'rb') as f:
            response = requests.post(
                config.COMMON_FORMS_API_URL, 
                files={'file': f}, 
                stream=True, 
                timeout=60
            )
            
            for line in response.iter_lines():
                if not line: continue
                data = json.loads(line)
                if data.get("status") == "success":
                    fields_by_page[data["page"]] = data.get("fields", [])
                elif data.get("status") == "error":
                    print(f"Model API Error on page {data.get('page')}: {data.get('msg')}")
                    
    except Exception as e:
        print(f"API Connection Error: {e}")
        return None
        
    return fields_by_page


def get_pdf_metadata(doc, filename: str):
    """
    Extract PDF metadata including page sizes for ClaiPDFCollection.
    """
    page_sizes = []
    for page in doc:
        rect = page.rect
        page_sizes.append({
            "rotation": page.rotation,
            "width": rect.width,
            "height": rect.height
        })
    
    # Get title from PDF metadata or use filename
    pdf_title = doc.metadata.get("title", "") if doc.metadata else ""
    if not pdf_title:
        pdf_title = os.path.splitext(filename)[0] if filename else "Document"
    
    return {
        "name": filename or "document.pdf",
        "title": pdf_title,
        "pageSizes": page_sizes
    }


def resolve_intermediate_format(all_fields, pdf_metadata):
    """
    Returns an intermediate format that will be transformed to ClaiSchema 
    in the Next.js layer. Uses tempIds for internal reference.
    
    This format is consumed by transform-to-clai-schema.ts which generates
    proper ClaiSchema-compliant IDs using TypeScript utilities.
    """
    groups, updated_fields = group_fields_by_section(all_fields)

    participants = {}
    final_fields = []
    routing_counter = 1
    
    for f in updated_fields:
        raw_role = str(f.get("role", "System")).strip().title()
        participant_temp_id = None
        
        if raw_role.lower() not in ["system", "n/a", "unknown", "none", ""]:
            participant_temp_id = f"part_{raw_role.lower().replace(' ', '_')}"
            if participant_temp_id not in participants:
                participants[participant_temp_id] = {
                    "tempId": participant_temp_id,
                    "role": "signer",
                    "type": "unknown",
                    "label": raw_role,
                    "routingOrder": routing_counter,
                    "definer": "PREPARER"
                }
                routing_counter += 1

        final_fields.append({
            "tempId": f["id"],
            "aliasId": f.get("aliasId"),
            "groupTempId": f.get("groupId"),
            "participantTempId": participant_temp_id, 
            "label": f["label"],
            "semanticType": f["semanticType"],
            "isDynamic": f.get("isDynamic", False),
            "page": f["page"],
            "rect": f["rect"]
        })
    
    # Transform groups to use tempId
    groups_with_temp_ids = []
    for g in groups:
        groups_with_temp_ids.append({
            "tempId": g["id"],
            "title": g["title"],
            "fieldTempIds": g["fieldIds"]
        })
        
    return {
        "participants": list(participants.values()),
        "groups": groups_with_temp_ids,
        "fields": final_fields,
        "pdfMetadata": pdf_metadata
    }

# ==============================================================================
# API ENDPOINT (Replaces async main())
# ==============================================================================

@app.post("/process-pdf")
async def process_pdf(file: UploadFile = File(...)):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
        shutil.copyfileobj(file.file, tmp)
        tmp_path = tmp.name

    doc = None
    try:
        utils.setup_debug_dir()

        print(f"Starting process for uploaded file: {file.filename}")
        raw_fields = await asyncio.to_thread(get_fields_from_local_api, tmp_path)
        
        if not raw_fields:
            raise HTTPException(status_code=500, detail="Failed to extract fields from Model API (Local Port 8000).")
        
        doc = fitz.open(tmp_path)
        
        # Extract PDF metadata for ClaiPDFCollection
        pdf_metadata = get_pdf_metadata(doc, file.filename)
        
        # Extract text context for vision processing
        text_sample = ""
        for i in range(min(2, len(doc))): 
            text_sample += doc[i].get_text()
        global_ctx = " ".join(text_sample.split())[:1500]

        # Process pages with vision and mapping
        semaphore = asyncio.Semaphore(config.MAX_CONCURRENT_PAGES)
        tasks = []
        for page_num, fields in raw_fields.items():
            tasks.append(process_page_smart(semaphore, doc, page_num, fields, global_ctx))
        
        results = await asyncio.gather(*tasks)
        flat_results = [item for sublist in results for item in sublist]
        mapped_results = await map_fields_to_schema(flat_results)
        
        # Return intermediate format for Next.js transformation
        intermediate_response = resolve_intermediate_format(mapped_results, pdf_metadata)
        
        return intermediate_response

    except Exception as e:
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

    finally:
        if doc:
            doc.close()
        if os.path.exists(tmp_path):
            os.remove(tmp_path)
        print(f"Cleanup complete for {tmp_path}")