Create app.py

app.py

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+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)