| """ |
| ValuationAI — Nairobi Valuation Sheet OCR |
| Model: rasmodev/Handwriting_trocr_model |
| """ |
| import io, time, logging, tempfile, os |
| import streamlit as st |
| import pandas as pd |
| from PIL import Image |
|
|
| st.set_page_config( |
| page_title="ValuationAI", |
| page_icon="📋", |
| layout="wide", |
| initial_sidebar_state="collapsed", |
| ) |
| logging.basicConfig(level=logging.INFO) |
|
|
| st.markdown(""" |
| <style> |
| @import url('https://fonts.googleapis.com/css2?family=Cormorant+Garamond:ital,wght@0,600;0,700;1,600&family=Inter:wght@300;400;500;600&display=swap'); |
| |
| html, body, [class*="css"], .stApp { |
| font-family: 'Inter', sans-serif; |
| background: #F8F7F4; |
| color: #1A1A2E; |
| } |
| .block-container { |
| padding: 3rem 4rem !important; |
| max-width: 1100px !important; |
| } |
| #MainMenu, footer, header { visibility: hidden; } |
| |
| .topbar { |
| display: flex; align-items: flex-end; |
| justify-content: space-between; |
| padding-bottom: 2rem; margin-bottom: 3rem; |
| border-bottom: 2px solid #1A1A2E; |
| } |
| .logo { font-family: 'Cormorant Garamond', serif; font-size: 1.8rem; font-weight: 700; color: #1A1A2E; letter-spacing: -0.02em; line-height: 1; } |
| .logo span { color: #2563EB; } |
| .logo-sub { font-size: 0.68rem; font-weight: 500; letter-spacing: 0.15em; text-transform: uppercase; color: #9CA3AF; margin-top: 0.3rem; } |
| .model-ref { font-size: 0.7rem; color: #9CA3AF; font-weight: 400; letter-spacing: 0.04em; text-align: right; } |
| .model-ref strong { color: #2563EB; font-weight: 600; } |
| |
| .headline { font-family: 'Cormorant Garamond', serif; font-size: 3.4rem; font-weight: 700; line-height: 1.08; letter-spacing: -0.03em; color: #1A1A2E; margin-bottom: 1rem; max-width: 700px; } |
| .headline em { font-style: italic; color: #2563EB; } |
| .subline { font-size: 0.95rem; font-weight: 300; color: #6B7280; line-height: 1.7; max-width: 500px; margin-bottom: 3rem; } |
| |
| .step { font-size: 0.65rem; font-weight: 700; letter-spacing: 0.18em; text-transform: uppercase; color: #2563EB; margin-bottom: 0.5rem; } |
| |
| [data-testid="stFileUploader"] section { |
| background: #fff !important; |
| border: 2px dashed #D1D5DB !important; |
| border-radius: 12px !important; |
| padding: 2.5rem !important; |
| transition: all 0.2s ease !important; |
| box-shadow: 0 1px 3px rgba(0,0,0,0.04) !important; |
| } |
| [data-testid="stFileUploader"] section:hover { |
| border-color: #2563EB !important; |
| box-shadow: 0 0 0 4px rgba(37,99,235,0.06) !important; |
| } |
| |
| .fchip { display: inline-flex; align-items: center; gap: 5px; background: #EFF6FF; border: 1px solid #BFDBFE; color: #1D4ED8; padding: 0.25rem 0.7rem; border-radius: 6px; font-size: 0.73rem; font-weight: 500; margin: 2px; } |
| |
| .stButton > button { |
| background: #1A1A2E !important; color: #fff !important; border: none !important; |
| border-radius: 8px !important; padding: 0.85rem 2.5rem !important; |
| font-family: 'Inter', sans-serif !important; font-size: 0.88rem !important; |
| font-weight: 600 !important; letter-spacing: 0.04em !important; |
| text-transform: uppercase !important; transition: all 0.2s !important; |
| box-shadow: 0 2px 8px rgba(26,26,46,0.2) !important; width: 100% !important; |
| } |
| .stButton > button:hover { background: #2563EB !important; box-shadow: 0 4px 16px rgba(37,99,235,0.3) !important; transform: translateY(-1px) !important; } |
| .stButton > button:disabled { background: #E5E7EB !important; color: #9CA3AF !important; box-shadow: none !important; transform: none !important; } |
| |
| .stProgress > div > div > div { background: #2563EB !important; border-radius: 4px !important; } |
| .stProgress > div > div { background: #E5E7EB !important; border-radius: 4px !important; height: 4px !important; } |
| |
| .stats-strip { display: flex; background: #1A1A2E; border-radius: 12px; overflow: hidden; margin: 2.5rem 0 2rem; } |
| .stat-item { flex: 1; padding: 1.6rem 2rem; border-right: 1px solid rgba(255,255,255,0.08); } |
| .stat-item:last-child { border-right: none; } |
| .stat-n { font-family: 'Cormorant Garamond', serif; font-size: 2.6rem; font-weight: 700; color: #fff; line-height: 1; margin-bottom: 0.3rem; } |
| .stat-l { font-size: 0.68rem; font-weight: 500; letter-spacing: 0.12em; text-transform: uppercase; color: #6B7280; } |
| |
| .section-head { display: flex; align-items: center; justify-content: space-between; margin-bottom: 1rem; padding-bottom: 0.75rem; border-bottom: 1px solid #E5E7EB; } |
| .section-title { font-family: 'Cormorant Garamond', serif; font-size: 1.5rem; font-weight: 600; color: #1A1A2E; } |
| |
| div[data-testid="stDownloadButton"] > button { |
| background: #fff !important; border: 1.5px solid #1A1A2E !important; color: #1A1A2E !important; |
| border-radius: 8px !important; padding: 0.6rem 1.4rem !important; |
| font-family: 'Inter', sans-serif !important; font-weight: 600 !important; |
| font-size: 0.82rem !important; letter-spacing: 0.04em !important; |
| text-transform: uppercase !important; transition: all 0.2s !important; width: auto !important; |
| } |
| div[data-testid="stDownloadButton"] > button:hover { background: #1A1A2E !important; color: #fff !important; } |
| |
| [data-testid="stDataFrame"] { border-radius: 10px !important; border: 1px solid #E5E7EB !important; overflow: hidden !important; box-shadow: 0 1px 4px rgba(0,0,0,0.05) !important; } |
| </style> |
| """, unsafe_allow_html=True) |
|
|
|
|
| |
| |
| |
| @st.cache_resource(show_spinner="Loading recognition model…") |
| def load_model(): |
| import torch |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel |
| MODEL_ID = "rasmodev/Handwriting_trocr_model" |
| BASE_ID = "microsoft/trocr-base-handwritten" |
| |
| |
| processor = TrOCRProcessor.from_pretrained(BASE_ID) |
| model = VisionEncoderDecoderModel.from_pretrained(MODEL_ID) |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model.to(device).eval() |
| return processor, model, device |
|
|
|
|
| |
| |
| |
| def ocr_page(img: Image.Image) -> str: |
| import torch |
| processor, model, device = load_model() |
| pixel_values = processor( |
| images=img.convert("RGB"), |
| return_tensors="pt" |
| ).pixel_values.to(device) |
| with torch.no_grad(): |
| generated = model.generate( |
| pixel_values=pixel_values, |
| max_new_tokens=64, |
| num_beams=1, |
| ) |
| return processor.batch_decode(generated, skip_special_tokens=True)[0].strip() |
|
|
|
|
| |
| |
| |
| |
| def parse_label(raw_text: str, filename: str) -> dict: |
| record = { |
| "File": filename, |
| "Plot Number": "", |
| "Location": "", |
| "Area": "", |
| "Amount (KES)": None, |
| "Date": "", |
| "VOS": "", |
| "Raw Output": raw_text, |
| } |
| for part in raw_text.split("|"): |
| part = part.strip() |
| if ":" not in part: |
| continue |
| key, _, val = part.partition(":") |
| key = key.strip().upper() |
| val = val.strip() |
| if key == "PLOT": |
| record["Plot Number"] = val |
| elif key == "LOC": |
| record["Location"] = val |
| elif key == "AREA": |
| record["Area"] = val |
| elif key == "AMT": |
| try: |
| record["Amount (KES)"] = int(val.replace(",", "").replace(" ", "")) |
| except ValueError: |
| record["Amount (KES)"] = val |
| elif key == "DATE": |
| record["Date"] = val |
| elif key == "VOS": |
| record["VOS"] = val |
| return record |
|
|
|
|
| |
| |
| |
| def make_excel(records: list) -> bytes: |
| from openpyxl import load_workbook |
| from openpyxl.styles import Font, PatternFill, Alignment |
| from openpyxl.utils import get_column_letter |
| clean = [{k: v for k, v in r.items() if k != "Raw Output"} for r in records] |
| buf = io.BytesIO() |
| pd.DataFrame(clean).to_excel(buf, index=False, sheet_name="Valuation Data") |
| buf.seek(0) |
| wb = load_workbook(buf) |
| ws = wb.active |
| hdr = PatternFill("solid", start_color="1A1A2E") |
| for ci, cell in enumerate(ws[1], 1): |
| cell.font = Font(name="Calibri", bold=True, color="FFFFFF", size=11) |
| cell.fill = hdr |
| cell.alignment = Alignment(horizontal="center", vertical="center") |
| ws.column_dimensions[get_column_letter(ci)].width = 26 |
| ws.row_dimensions[1].height = 30 |
| for row in ws.iter_rows(min_row=2): |
| for cell in row: |
| cell.alignment = Alignment(vertical="center", wrap_text=True) |
| if cell.row % 2 == 0: |
| cell.fill = PatternFill("solid", start_color="F0F4FF") |
| ws.freeze_panes = "A2" |
| out = io.BytesIO() |
| wb.save(out) |
| return out.getvalue() |
|
|
|
|
| |
| |
| |
| for k, v in [("records",[]),("excel",None),("done",False),("errors",[])]: |
| if k not in st.session_state: |
| st.session_state[k] = v |
|
|
|
|
| |
| |
| |
| st.markdown(""" |
| <div class="topbar"> |
| <div> |
| <div class="logo">Valuation<span>AI</span></div> |
| <div class="logo-sub">Nairobi City County — Document Intelligence</div> |
| </div> |
| <div class="model-ref"> |
| Recognition model<br> |
| <strong>rasmodev/Handwriting_trocr_model</strong> |
| </div> |
| </div> |
| """, unsafe_allow_html=True) |
|
|
| st.markdown(""" |
| <div class="headline"> |
| Digitise handwritten<br>valuation sheets <em>instantly.</em> |
| </div> |
| <div class="subline"> |
| Upload one or more scanned PDF valuation sheets. |
| The system reads every handwritten field and delivers |
| a structured Excel file — ready for records management. |
| </div> |
| """, unsafe_allow_html=True) |
|
|
| st.markdown('<div class="step">Step 1 — Upload Documents</div>', unsafe_allow_html=True) |
|
|
| uploaded = st.file_uploader( |
| "Drag and drop valuation sheet PDFs here, or click to browse", |
| type=["pdf", "png", "jpg", "jpeg", "tiff", "bmp"], |
| accept_multiple_files=True, |
| label_visibility="collapsed", |
| ) |
|
|
| if uploaded: |
| chips = "".join( |
| f'<span class="fchip">📄 {f.name[:35]}{"…" if len(f.name)>35 else ""}</span>' |
| for f in uploaded |
| ) |
| st.markdown(f'<div style="margin-top:0.6rem">{chips}</div>', unsafe_allow_html=True) |
|
|
| st.markdown('<div style="height:1.2rem"></div>', unsafe_allow_html=True) |
| st.markdown('<div class="step">Step 2 — Extract & Download</div>', unsafe_allow_html=True) |
|
|
| run = st.button( |
| "Extract Data from Documents", |
| disabled=not uploaded, |
| use_container_width=True, |
| ) |
|
|
|
|
| |
| |
| |
| if run and uploaded: |
| import fitz, traceback |
|
|
| st.session_state.records = [] |
| st.session_state.errors = [] |
| st.session_state.done = False |
|
|
| bar = st.progress(0.0) |
| status = st.empty() |
| t0 = time.time() |
|
|
| for fi, uf in enumerate(uploaded): |
| fname = uf.name |
| raw = uf.read() |
| bar.progress(fi / len(uploaded), text=f"Reading {fname}…") |
|
|
| st.write(f"📄 **{fname}** — {len(raw):,} bytes") |
|
|
| try: |
| ext = fname.lower().rsplit(".", 1)[-1] |
|
|
| if ext == "pdf": |
| |
| with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp: |
| tmp.write(raw) |
| tmp_path = tmp.name |
|
|
| doc = fitz.open(tmp_path) |
| st.write(f" ✅ PDF opened — {len(doc)} page(s) found") |
|
|
| imgs = [] |
| mat = fitz.Matrix(200/72, 200/72) |
| for page in doc: |
| pix = page.get_pixmap(matrix=mat, alpha=False) |
| img = Image.open(io.BytesIO(pix.tobytes("png"))).convert("RGB") |
| imgs.append(img) |
| pix = None |
| doc.close() |
| os.unlink(tmp_path) |
| st.write(f" ✅ Rasterized {len(imgs)} page image(s)") |
|
|
| else: |
| imgs = [Image.open(io.BytesIO(raw)).convert("RGB")] |
| st.write(f" ✅ Loaded image") |
|
|
| if not imgs: |
| st.error(f" ❌ No pages extracted from {fname}") |
| st.session_state.errors.append(f"{fname}: no pages extracted") |
| continue |
|
|
| for pi, img in enumerate(imgs, 1): |
| status.caption(f"Running OCR on **{fname}** — page {pi} of {len(imgs)}") |
| raw_text = ocr_page(img) |
| st.write(f" 📝 Page {pi} OCR output: `{raw_text}`") |
| record = parse_label(raw_text, fname) |
| st.session_state.records.append(record) |
|
|
| except Exception as e: |
| st.error(f"❌ Error on {fname}: {e}") |
| st.code(traceback.format_exc()) |
| st.session_state.errors.append(f"{fname}: {e}") |
|
|
| bar.progress((fi + 1) / len(uploaded)) |
|
|
| bar.empty() |
| status.empty() |
|
|
| if st.session_state.records: |
| st.session_state.excel = make_excel(st.session_state.records) |
|
|
| st.session_state.done = True |
| elapsed = time.time() - t0 |
| st.success( |
| f"Processed {len(st.session_state.records)} page(s) " |
| f"from {len(uploaded)} document(s) in {elapsed:.1f}s." |
| ) |
|
|
|
|
| |
| |
| |
| if st.session_state.done and st.session_state.records: |
| records = st.session_state.records |
| df = pd.DataFrame(records) |
| display_cols = [c for c in df.columns if c != "Raw Output"] |
| df_display = df[display_cols] |
|
|
| n_plots = df["Plot Number"].astype(bool).sum() |
| n_amounts = pd.to_numeric(df["Amount (KES)"], errors="coerce").notna().sum() |
| n_dates = df["Date"].astype(bool).sum() |
|
|
| st.markdown(f""" |
| <div class="stats-strip"> |
| <div class="stat-item"><div class="stat-n">{len(records)}</div><div class="stat-l">Pages processed</div></div> |
| <div class="stat-item"><div class="stat-n">{n_plots}</div><div class="stat-l">Plot numbers</div></div> |
| <div class="stat-item"><div class="stat-n">{n_amounts}</div><div class="stat-l">Amounts extracted</div></div> |
| <div class="stat-item"><div class="stat-n">{n_dates}</div><div class="stat-l">Dates captured</div></div> |
| </div> |
| """, unsafe_allow_html=True) |
|
|
| col_t, col_d = st.columns([5, 1]) |
| with col_t: |
| st.markdown('<div class="section-head"><div class="section-title">Extracted Records</div></div>', unsafe_allow_html=True) |
| with col_d: |
| st.markdown('<div style="padding-top:0.3rem"></div>', unsafe_allow_html=True) |
| if st.session_state.excel: |
| st.download_button( |
| "⬇ Export Excel", |
| data=st.session_state.excel, |
| file_name="valuation_records.xlsx", |
| mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", |
| ) |
|
|
| st.dataframe(df_display, use_container_width=True, |
| height=min(80 + len(df)*38, 560), hide_index=True) |
|
|
| with st.expander("🔍 Raw model output (for verification)"): |
| for r in records: |
| st.markdown( |
| f'<div style="font-family:monospace;font-size:0.78rem;' |
| f'padding:0.5rem 0;border-bottom:1px solid #E5E7EB;color:#374151">' |
| f'<strong>{r["File"]}</strong><br>{r.get("Raw Output","")}</div>', |
| unsafe_allow_html=True, |
| ) |
|
|
| if st.session_state.errors: |
| with st.expander(f"⚠ {len(st.session_state.errors)} file(s) could not be processed"): |
| for e in st.session_state.errors: |
| st.caption(e) |
