app.py

import cv2
import numpy as np
import pytesseract
import requests
import pandas as pd
import gradio as gr
import uuid
import os

# ──────────────────────────────────────────────────────────────
# 1. OCR on the full image (always)
# ──────────────────────────────────────────────────────────────
def ocr_full_image(image: np.ndarray) -> str:
    """
    Run Tesseract OCR on the entire image (no thresholding).
    Return the raw OCR text.
    """
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Note: we’re NOT thresholding here—sometimes stylized covers lose detail under THRESH_OTSU.
    text = pytesseract.image_to_string(gray, config="--oem 3 --psm 6")
    return text.strip()

# ──────────────────────────────────────────────────────────────
# 2. Query OpenLibrary API
# ──────────────────────────────────────────────────────────────
def query_openlibrary(title_text: str, author_text: str = None) -> dict | 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

# ──────────────────────────────────────────────────────────────
# 3. Process one uploaded image (single OCR pass)
# ──────────────────────────────────────────────────────────────
def process_image(image_file):
    """
    Gradio passes a PIL image or numpy array. Convert to OpenCV BGR, 
    OCR the entire image, parse first two lines for title/author,
    query OpenLibrary once, and return a DataFrame + CSV file path.
    """
    # Convert PIL to OpenCV BGR
    img = np.array(image_file)[:, :, ::-1].copy()

    # 1) Run OCR on full image
    full_text = ocr_full_image(img)
    lines = [line.strip() for line in full_text.splitlines() if line.strip()]

    records = []
    if lines:
        # Use first line as title, second (if exists) as author
        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:
            # No match → still include OCR guesses
            records.append({
                "title": title_guess,
                "author_name": author_guess or "",
                "publisher": "",
                "first_publish_year": "",
            })

    # Build DataFrame (even if empty)
    df = pd.DataFrame(records, columns=["title", "author_name", "publisher", "first_publish_year"])
    csv_bytes = df.to_csv(index=False).encode()

    # Write CSV to a unique temporary file
    unique_name = f"books_{uuid.uuid4().hex}.csv"
    temp_path = os.path.join("/tmp", unique_name)
    with open(temp_path, "wb") as f:
        f.write(csv_bytes)

    return df, temp_path

# ──────────────────────────────────────────────────────────────
# 4. Build the Gradio Interface
# ──────────────────────────────────────────────────────────────
def build_interface():
    with gr.Blocks(title="Book Cover OCR + Lookup (Single‐Cover Mode)") as demo:
        gr.Markdown(
            """
            ## Book Cover OCR + OpenLibrary Lookup

            1. Upload a photo of a single book cover (or any cover‐style image).  
            2. The app will run OCR on the full image, take:
               - the **first line** as a “title” guess, and  
               - the **second line** (if any) as an “author” guess, then  
               - query OpenLibrary once for metadata.  
            3. You’ll see the result in a table and can download a CSV.  

            > **Note:**  
            > • Because we skip rectangle detection, any visible text on your cover (large, legible fonts) should be picked up.  
            > • If you have multiple covers in one photo, only the first “title/author” will be used.
            """
        )

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

        output_table = gr.Dataframe(
            headers=["title", "author_name", "publisher", "first_publish_year"],
            label="Detected Book Metadata",
            datatype="pandas",
        )
        download_file = gr.File(label="Download CSV")

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

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

    return demo

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