app.py

import cv2
import numpy as np
import pytesseract
import requests
import pandas as pd
import gradio as gr
from io import BytesIO

# ──────────────────────────────────────────────────────────────
# 1. Utility: Detect rectangular contours (approximate book covers)
# ──────────────────────────────────────────────────────────────
def detect_book_regions(image: np.ndarray, min_area=10000, eps_coef=0.02):
    """
    Detect rectangular regions in an image that likely correspond to book covers.
    Returns a list of bounding boxes: (x, y, w, h).
    """
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blurred, 50, 150)

    # Dilate + erode to close gaps
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)

    contours, _ = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    boxes = []

    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area < min_area:
            continue

        peri = cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, eps_coef * peri, True)

        # Keep only quadrilaterals
        if len(approx) == 4:
            x, y, w, h = cv2.boundingRect(approx)
            ar = w / float(h)
            # Filter by typical book-cover aspect ratios
            if 0.4 < ar < 0.9 or 1.0 < ar < 1.6:
                boxes.append((x, y, w, h))

    # Sort left→right, top→bottom
    boxes = sorted(boxes, key=lambda b: (b[1], b[0]))
    return boxes

# ──────────────────────────────────────────────────────────────
# 2. OCR on a cropped region
# ──────────────────────────────────────────────────────────────
def ocr_on_region(image: np.ndarray, box: tuple):
    """
    Crop the image to the given box and run Tesseract OCR.
    Return the raw OCR text.
    """
    x, y, w, h = box
    cropped = image[y:y + h, x:x + w]
    gray_crop = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
    _, thresh_crop = cv2.threshold(gray_crop, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    custom_config = r'--oem 3 --psm 6'
    text = pytesseract.image_to_string(thresh_crop, config=custom_config)
    return text.strip()

# ──────────────────────────────────────────────────────────────
# 3. Query OpenLibrary API
# ──────────────────────────────────────────────────────────────
def query_openlibrary(title_text: str, author_text: str = None):
    """
    Search OpenLibrary by title (and optional author).
    Return a dict with title, author_name, publisher, first_publish_year, or None.
    """
    base_url = "https://openlibrary.org/search.json"
    params = {"title": title_text}
    if author_text:
        params["author"] = author_text

    try:
        resp = requests.get(base_url, params=params, timeout=5)
        resp.raise_for_status()
        data = resp.json()
        if data.get("docs"):
            doc = data["docs"][0]
            return {
                "title": doc.get("title", ""),
                "author_name": ", ".join(doc.get("author_name", [])),
                "publisher": ", ".join(doc.get("publisher", [])),
                "first_publish_year": doc.get("first_publish_year", "")
            }
    except Exception as e:
        print(f"OpenLibrary query failed: {e}")

    return None

# ──────────────────────────────────────────────────────────────
# 4. Process one uploaded image
# ──────────────────────────────────────────────────────────────
def process_image(image_file):
    """
    Gradio passes a PIL image or numpy array. Convert to OpenCV BGR, detect covers → OCR → OpenLibrary.
    Return a DataFrame and CSV bytes.
    """
    img = np.array(image_file)[:, :, ::-1].copy()  # PIL to OpenCV BGR
    boxes = detect_book_regions(img)
    records = []

    for box in boxes:
        ocr_text = ocr_on_region(img, box)
        lines = [l.strip() for l in ocr_text.splitlines() if l.strip()]
        if not lines:
            continue

        title_guess = lines[0]
        author_guess = lines[1] if len(lines) > 1 else None
        meta = query_openlibrary(title_guess, author_guess)

        if meta:
            records.append(meta)
        else:
            records.append({
                "title": title_guess,
                "author_name": author_guess or "",
                "publisher": "",
                "first_publish_year": "",
            })

    if not records:
        df_empty = pd.DataFrame(columns=["title", "author_name", "publisher", "first_publish_year"])
        return df_empty, df_empty.to_csv(index=False).encode()

    df = pd.DataFrame(records)
    csv_bytes = df.to_csv(index=False).encode()
    return df, csv_bytes

# ──────────────────────────────────────────────────────────────
# 5. Build the Gradio Interface
# ──────────────────────────────────────────────────────────────
def build_interface():
    with gr.Blocks(title="Book Cover Scanner") as demo:
        gr.Markdown(
            """
            ## Book Cover Scanner + Metadata Lookup
            1. Upload a photo containing one or multiple book covers  
            2. The app will detect each cover, run OCR, then query OpenLibrary for metadata  
            3. Results appear in a table below, and you can download a CSV  
            """
        )

        with gr.Row():
            img_in = gr.Image(type="pil", label="Upload Image of Book Covers")
            run_button = gr.Button("Scan & Lookup")

        output_table = gr.Dataframe(
            headers=["title", "author_name", "publisher", "first_publish_year"],
            label="Detected Books with Metadata"
        )
        download_btn = gr.Download(label="Download CSV")

        def on_run(image):
            df, csv_bytes = process_image(image)
            return df, csv_bytes

        run_button.click(fn=on_run, inputs=[img_in], outputs=[output_table, download_btn])

    return demo

if __name__ == "__main__":
    demo_app = build_interface()
    demo_app.launch()