import sys,os,json,pickle,csv,numpy as np
S=os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0,S)
from ocr_fuse import norm,sim  # noqa (module runs OCR of packshots on import; cheap)
sig=pickle.load(open(f'{S}/sig.pkl','rb')); O=pickle.load(open(f'{S}/ocr.pkl','rb'))
L={f['index']:f['label'] for f in json.load(open(f'{S}/../../../../eval/labels/image6.json'))['facings']}
def conf(ev):
    r=sorted(ev.items(),key=lambda kv:-kv[1]['inliers']*(0.25+kv[1]['color']))
    (k1,e1),(k2,e2)=r[0],r[1]; i1,i2=e1['inliers'],e2['inliers']
    return k1, i1*(1-i2/max(i1,1))*e1['color']
for res,up,run in [('full',1,'v_ms_root_aff_full.pkl'),('live',2,'v_ms_root_aff_live.pkl')]:
    ocr={r['index']:r['text'] for r in O[res] if r['up']==up}
    M={x['index']:x for x in pickle.load(open(f'{S}/{run}','rb'))['rows']}
    n_in=sum(1 for t in L.values() if t not in ('unknown','exclude'))
    # OCR-alone identity among ALL catalog SKUs
    ok=tot=0
    for i,t in L.items():
        if t in ('unknown','exclude') or i not in ocr: continue
        tx=ocr[i]; 
        if len(norm(tx))<4: continue
        s={k:sim(tx,v) for k,v in sig.items()}; tot+=1; ok+= max(s,key=s.get)==t
    print(f'== {res}: OCR-alone top-1 over all 12 catalog SKUs, on facings with text: {ok}/{tot}')
    for delta in [None,0.0,0.1,0.2]:
        line=[]
        for tau in [0.5,1,2,3]:
            c=a=0
            for i,t in L.items():
                if t=='exclude' or i not in M or not M[i]['ev']: continue
                k,cf=conf(M[i]['ev'])
                if cf<tau: continue
                if delta is not None and len(norm(ocr.get(i,'')))>=4:
                    s={kk:sim(ocr[i],v) for kk,v in sig.items()}; j=max(s,key=s.get)
                    if j!=k and s[j]-s[k]>delta: continue   # text names a different catalog product: veto
                a+=1; c+= k==t
            line.append(f'tau{tau}: {c}/{n_in} p={c/max(a,1):.0%}')
        print(' veto' if delta is not None else ' matcher only', delta, ' | '.join(line))
print('== union: matcher (veto) OR OCR-only identity')
for res,up,run in [('full',1,'v_ms_root_aff_full.pkl'),('live',2,'v_ms_root_aff_live.pkl')]:
    ocr={r['index']:r['text'] for r in O[res] if r['up']==up}
    M={x['index']:x for x in pickle.load(open(f'{S}/{run}','rb'))['rows']}
    n_in=sum(1 for t in L.values() if t not in ('unknown','exclude'))
    for tau in [0.5,1,2]:
        for sg,mu in [(0.5,0.15),(0.6,0.2),(0.7,0.25)]:
            c=a=0; fa_unk=0; bysrc={'m':0,'o':0}
            for i,t in L.items():
                if t=='exclude' or i not in M: continue
                tx=ocr.get(i,''); s={kk:sim(tx,v) for kk,v in sig.items()} if len(norm(tx))>=4 else {}
                j=max(s,key=s.get) if s else None
                ans=None
                if M[i]['ev']:
                    k,cf=conf(M[i]['ev'])
                    if cf>=tau and not (j and j!=k and s[j]-s[k]>0): ans=k; src='m'
                if ans is None and s:
                    r=sorted(s.values(),reverse=True)
                    if r[0]>=sg and r[0]-r[1]>=mu: ans=j; src='o'
                if ans:
                    a+=1; c+= ans==t; bysrc[src]+= ans==t; fa_unk+= t=='unknown'
            print(f' {res} tau{tau} ocr>={sg} m{mu}: correct {c}/{n_in} ({c/n_in:.0%}) precision {c/max(a,1):.0%} accepted-unknown {fa_unk}/48 from {bysrc}')