Update app.py

app.py

@@ -3,38 +3,44 @@ from pdf2image import convert_from_path
 from PIL import Image
 import pytesseract
 from transformers import pipeline
+import tempfile
+import os
 
-
-
-# Load summarization pipeline
+# Load Hugging Face summarization pipeline
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Function to split text into chunks within model token limit
+# Chunk text into smaller pieces for model input limit
 def chunk_text(text, max_tokens=1000):
     words = text.split()
     for i in range(0, len(words), max_tokens):
         yield " ".join(words[i:i+max_tokens])
 
-# Main function to handle PDF upload and summarize
+# Main function: PDF upload → OCR → Summarization
 def summarize_image_pdf(pdf_file):
     try:
-        # Convert PDF pages to images
-        images = convert_from_path(pdf_file.name, dpi=300)
+        # Save uploaded file temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
+            tmp.write(pdf_file.read())
+            tmp_path = tmp.name
 
-        # OCR: Extract text from each page
+        # Convert PDF to images (Poppler is pre-installed on Spaces)
+        images = convert_from_path(tmp_path, dpi=300)
+
+        # OCR: Extract text from images
         extracted_text = ""
         for i, img in enumerate(images):
             text = pytesseract.image_to_string(img)
             extracted_text += f"\n\n--- Page {i+1} ---\n{text}"
 
-        # Summarize in chunks
+        os.remove(tmp_path)  # Clean up temp file
+
+        # Summarize text
         summaries = []
         for chunk in chunk_text(extracted_text):
             summary = summarizer(chunk, max_length=130, min_length=30, do_sample=False)[0]['summary_text']
             summaries.append(summary)
 
-        final_summary = "\n\n".join(summaries)
-        return final_summary
+        return "\n\n".join(summaries)
 
     except Exception as e:
         return f"❌ Error: {str(e)}"
@@ -42,12 +48,10 @@ def summarize_image_pdf(pdf_file):
 # Gradio UI
 interface = gr.Interface(
     fn=summarize_image_pdf,
-    inputs=gr.File(label="📄 Upload Image-based PDF", type="file"),
+    inputs=gr.File(label="📄 Upload a scanned/image PDF", file_types=[".pdf"]),
     outputs=gr.Textbox(label="📝 Summary"),
-    title="🧠 Image PDF Summarizer with OCR",
-    description="This app extracts text from scanned/image-based PDFs using OCR and summarizes it using Hugging Face's BART model.",
+    title="📘 OCR PDF Summarizer",
+    description="Upload a scanned or image-based PDF. The app will extract text using OCR and summarize it using Hugging Face's BART model.",
 )
 
-# Launch app
-if __name__ == "__main__":
-    interface.launch()
+interface.launch()