Update app.py

app.py

@@ -1,24 +1,95 @@
+import io, os, re
+from typing import List, Dict
+import streamlit as st
+import pandas as pd
+
+# --- PDF text
+import pdfplumber
+from pypdf import PdfReader
+
+# --- OCR
+from pdf2image import convert_from_bytes
+import pytesseract
+from PIL import Image
+
+# ======================================================================
+# SCHEMA TABELLA
+# ======================================================================
+SCHEMA = [
+    "Piece","SKU","Title","Capacity","% Recycled","Weight","Color","Material / Resin","Class","Source File",
+    "Component","Function","General description of the packaging","Material Ref GCAS","Material Family"
+]
+
 # ======================================================================
-# WEIGHT PARSER → restituisce SOLO il valore, es. "94±3g" (no "Weight")
+# FUNZIONI BASE PDF / OCR
+# ======================================================================
+def extract_text_pages(pdf_bytes: bytes) -> List[str]:
+    try:
+        with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:
+            return [p.extract_text() or "" for p in pdf.pages]
+    except Exception:
+        pass
+    try:
+        reader = PdfReader(io.BytesIO(pdf_bytes))
+        return [(p.extract_text() or "") for p in reader.pages]
+    except Exception:
+        return []
+
+def run_ocr(pdf_bytes: bytes, lang: str, dpi: int, tesseract_cmd: str | None) -> List[str]:
+    if tesseract_cmd:
+        pytesseract.pytesseract.tesseract_cmd = tesseract_cmd
+    imgs = convert_from_bytes(pdf_bytes, dpi=dpi)
+    config = "--psm 6 -c preserve_interword_spaces=1"
+    return [pytesseract.image_to_string(im, lang=lang, config=config) or "" for im in imgs]
+
+# ======================================================================
+# PARSER CAMPI TESTUALI
+# ======================================================================
+SKU_RE = re.compile(r"\b(?:Name|SKU|Part(?:\s*No\.?)?)\s*[:#]?\s*([A-Z0-9\-_/\.]{5,})", re.I)
+TITLE_RE = re.compile(r"\bTitle\s*[:\-]\s*(.+)", re.I)
+CLASS_RE = re.compile(r"\bClass\s*([A-Za-z ]+)", re.I)
+
+def _first(text, pat): 
+    m = pat.search(text or "")
+    return m.group(1).strip() if m else ""
+
+def capacity_from(t): 
+    m = re.search(r"([0-9]+(?:[.,][0-9]+)?)\s*(L|Liter|ml|mL)\b", t or "", re.I)
+    if not m: return ""
+    return f"{m.group(1).replace(',', '.')} {m.group(2).upper().replace('LITER','L').replace('ML','ml')}"
+
+def color_from(t):
+    m = re.search(r"(?:Part\s*Color|Color)\s*[:\-]?\s*([A-Z ]{3,})", t, re.I)
+    if m: return m.group(1).strip()
+    m = re.search(r"\b([A-Z ]{4,}(?:GREEN|WHITE|BLACK|BLUE|RED|CLEAR)[A-Z ]*)\b", t)
+    return m.group(1).strip() if m else ""
+
+def material_from(t):
+    for l in (t or "").splitlines():
+        if re.search(r"\bRESIN\b", l, re.I): 
+            return l.strip()
+    m = re.search(r"(SERIOPLAST.*?RESIN)", t, re.I)
+    return m.group(1).strip() if m else ""
+
+# ======================================================================
+# WEIGHT PARSER → restituisce esattamente "94±3g"
 # ======================================================================
 WEIGHT_VALUE_RE = re.compile(
-    r"""(?ix)                       # i=ignorecase, x=verbose
+    r"""(?ix)
     \bweight\b
-    [^\n\r]{0,80}?                  # pochi caratteri sulla stessa riga
-    (                               # ======= GRUPPO CATTURATO =======
-        (?:\d+(?:[.,]\d+)?\s*       # valore principale es. 94 o 94,5
-           (?:±|\+/?-|\+-)\s*       # simbolo tolleranza: ±  o +/- o +-
-           \d+(?:[.,]\d+)?\s*       # tolleranza es. 3 o 3,0
-           (?:mg|g|kg)              # unità
-        )
+    [^\n\r]{0,80}?
+    (
+        (?:\d+(?:[.,]\d+)?\s*
+           (?:±|\+/?-|\+-)\s*
+           \d+(?:[.,]\d+)?\s*
+           (?:mg|g|kg))
         |
-        (?:\d+(?:[.,]\d+)?\s*(?:mg|g|kg))  # fallback: solo "94 g"
-    )                               # =================================
+        (?:\d+(?:[.,]\d+)?\s*(?:mg|g|kg))
+    )
     """,
 )
 
 def _normalize_weight(s: str) -> str:
-    # compatta spazi, normalizza simbolo e virgole → "94±3g"
     s = (s or "").strip()
     s = s.replace(" ", "")
     s = s.replace("+/-", "±").replace("+-", "±")
@@ -31,7 +102,6 @@ def weight_from(t: str) -> str:
     m = WEIGHT_VALUE_RE.search(t)
     if m:
         return _normalize_weight(m.group(1))
-    # Fallback riga-per-riga (OCR con spezzature strane)
     for line in (t or "").splitlines():
         if "weight" in line.lower():
             m2 = re.search(
@@ -41,3 +111,143 @@ def weight_from(t: str) -> str:
             if m2:
                 return _normalize_weight(m2.group(1))
     return ""
+
+# ======================================================================
+# ALTRE FUNZIONI
+# ======================================================================
+_ALLOWED_PIECES = {
+    "ribbon":"ribbon","bottle":"bottle","film bundle":"film bundle","container":"container",
+    "label - adhesive":"LABEL - ADHESIVE","label adhesive":"LABEL - ADHESIVE","label-adhesive":"LABEL - ADHESIVE",
+    "label - back":"LABEL - BACK","back label":"LABEL - BACK","label back":"LABEL - BACK","closure":"CLOSURE"
+}
+_PACK_COMP_TYPE_RE = re.compile(r"Packaging\s+Component\s+Type\s*[:\-]?\s*([^\n\r]+)", re.I)
+
+def _normalize_piece(s):
+    s2 = re.sub(r"\s+", " ", (s or "").strip().lower())
+    for k,v in _ALLOWED_PIECES.items():
+        if k in s2: return v
+    return ""
+
+def piece_from(t,cls):
+    m = _PACK_COMP_TYPE_RE.search(t or "")
+    if m:
+        val = _normalize_piece(m.group(1))
+        if val: return val
+    if cls:
+        if "bottle" in cls.lower(): return "bottle"
+        if "cap" in cls.lower(): return "CLOSURE"
+        if "corrugated" in cls.lower(): return "container"
+        if "label" in cls.lower(): return "LABEL - BACK"
+    return ""
+
+FUNCTION_RE = re.compile(r"\b(Primary|Secondary(?:\s*or\s*Tertiary)?|Tertiary)\b", re.I)
+
+def component_from(t,piece,cls):
+    txt = t.lower()
+    if "label" in txt: return "Labels"
+    if piece: return piece
+    if cls and "bottle" in cls.lower(): return "Bottle"
+    return ""
+
+def function_from(t):
+    m = FUNCTION_RE.search(t or "")
+    return m.group(1).title() if m else ""
+
+def material_ref_gcas_from(t):
+    m = re.findall(r"\b(\d{7,9})\b", t or "")
+    return ", ".join(sorted(set(m))) if m else ""
+
+def material_family_from(t):
+    fams = ["Monolayer HDPE","Polypropylene (PP)","Paper","Rigid Paper – Corrugated Case"]
+    for f in fams:
+        if f.lower() in (t or "").lower(): return f
+    if re.search(r"\bHDPE\b", t): return "Monolayer HDPE"
+    if re.search(r"\bPP\b", t, re.I): return "Polypropylene (PP)"
+    return ""
+
+# ======================================================================
+# PARSER PRINCIPALE
+# ======================================================================
+def parse_record(pages: List[str], source_name: str) -> Dict[str,str]:
+    full = "\n".join(pages or [""])
+    sku = _first(full, SKU_RE)
+    title = _first(full, TITLE_RE)
+    cls = _first(full, CLASS_RE)
+    cap = capacity_from(title) or capacity_from(full)
+    color = color_from(full)
+    material = material_from(full)
+    piece = piece_from(full, cls)
+    comp = component_from(full, piece, cls)
+    func = function_from(full)
+    gcas = material_ref_gcas_from(full)
+    mfam = material_family_from(full)
+    wght = weight_from(full)
+    return {
+        "Piece": piece or "","SKU": sku or "","Title": title or "",
+        "Capacity": cap or "","% Recycled": "–","Weight": wght or "–",
+        "Color": color or "","Material / Resin": material or "","Class": cls or "",
+        "Source File": source_name,"Component": comp or "","Function": func or "",
+        "General description of the packaging": "","Material Ref GCAS": gcas or "",
+        "Material Family": mfam or ""
+    }
+
+# ======================================================================
+# STREAMLIT UI
+# ======================================================================
+st.set_page_config(page_title="PDF → Table (OCR-ready)", layout="wide")
+st.title("📄→📊 PDF → Table (OCR-ready)")
+st.caption("Estrae automaticamente i campi, incluso il peso dalle immagini OCR.")
+
+with st.sidebar:
+    files = st.file_uploader("Seleziona PDF", type=["pdf"], accept_multiple_files=True)
+    st.markdown("---")
+    st.subheader("OCR")
+    ocr_fallback = st.checkbox("Usa OCR se non c'è testo", value=True)
+    ocr_lang = st.text_input("Lingue OCR (comma)", value="eng,ita")
+    ocr_dpi = st.number_input("DPI OCR", 200, 600, 300, 50)
+    tess_path = st.text_input("Percorso Tesseract (se non nel PATH)", value="")
+    run_btn = st.button("▶️ Estrai")
+
+if not run_btn:
+    st.info("Carica i PDF e premi **Estrai**.")
+    st.stop()
+if not files:
+    st.warning("Nessun PDF caricato.")
+    st.stop()
+
+lang = "+".join([p.strip() for p in ocr_lang.split(",") if p.strip()]) or "eng"
+tess_cmd = tess_path.strip() or None
+rows, errors = [], []
+
+for up in files:
+    try:
+        raw = up.read()
+        pages = extract_text_pages(raw)
+        if ocr_fallback and not any((p or "").strip() for p in pages):
+            pages = run_ocr(raw, lang=lang, dpi=int(ocr_dpi), tesseract_cmd=tess_cmd)
+        rec = parse_record(pages, up.name)
+        # se Weight vuoto, prova OCR diretto
+        if (not rec.get("Weight") or rec["Weight"] == "–") and ocr_fallback:
+            ocr_pages = run_ocr(raw, lang=lang, dpi=int(ocr_dpi), tesseract_cmd=tess_cmd)
+            w_ocr = weight_from("\n".join(ocr_pages))
+            if w_ocr: rec["Weight"] = w_ocr
+        rows.append(rec)
+    except Exception as e:
+        errors.append((up.name, str(e)))
+
+if errors:
+    with st.expander("Errori"):
+        for n,e in errors: st.error(f"{n}: {e}")
+
+df = pd.DataFrame(rows, columns=SCHEMA)
+st.success(f"Creat{ 'e' if len(df)!=1 else 'a' } {len(df)} riga/e.")
+st.dataframe(df,use_container_width=True)
+
+c1,c2 = st.columns(2)
+with c1:
+    st.download_button("⬇️ CSV", df.to_csv(index=False).encode("utf-8"), "table.csv", "text/csv")
+with c2:
+    bio = io.BytesIO()
+    with pd.ExcelWriter(bio, engine="openpyxl") as xw:
+        df.to_excel(xw, index=False, sheet_name="data")
+    st.download_button("⬇️ Excel", bio.getvalue(), "table.xlsx", "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet")