Update app.py

app.py

@@ -4,6 +4,8 @@ import pytesseract
 import requests
 import pandas as pd
 import gradio as gr
+import uuid
+import os
 
 # ──────────────────────────────────────────────────────────────
 # 1. Utility: Detect rectangular contours (approximate book covers)
@@ -100,7 +102,7 @@ def query_openlibrary(title_text: str, author_text: str = None):
 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 a (filename, bytes) tuple for CSV.
+    Write CSV to a temp file and return (DataFrame, filepath).
     """
     img = np.array(image_file)[:, :, ::-1].copy()  # PIL to OpenCV BGR
     boxes = detect_book_regions(img)
@@ -131,7 +133,14 @@ def process_image(image_file):
     # 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()
-    return df, ("books.csv", csv_bytes)
+
+    # Write 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
 
 # ──────────────────────────────────────────────────────────────
 # 5. Build the Gradio Interface
@@ -159,8 +168,8 @@ def build_interface():
         download_file = gr.File(label="Download CSV")
 
         def on_run(image):
-            df, file_tuple = process_image(image)
-            return df, file_tuple
+            df, filepath = process_image(image)
+            return df, filepath
 
         run_button.click(
             fn=on_run,