Update app.py

app.py

@@ -10,45 +10,53 @@ model_name = "pszemraj/led-large-book-summary"
 summarizer = pipeline("summarization", model=model_name, tokenizer=model_name)
 
 def extract_abstract_and_summarize(pdf_file):
-    try:
-        with open(pdf_file, "rb") as file:
-            pdf_reader = PdfReader(file)
-            abstract_text = ""
-            for page_num in range(len(pdf_reader.pages)):
-                page = pdf_reader.pages[page_num]
-                text = page.extract_text()
-                abstract_match = re.search(r"\bAbstract\b", text, re.IGNORECASE)
-                if abstract_match:
-                    start_index = abstract_match.end()
-                    introduction_match = re.search(r"\bIntroduction\b", text[start_index:], re.IGNORECASE)
-                    if introduction_match:
-                        end_index = start_index + introduction_match.start()
-                    else:
-                        end_index = None
-                    abstract_text = text[start_index:end_index]
-                    break
-
-            # Summarize the extracted abstract using the LED-large model
-            result = summarizer(abstract_text, max_length=256, min_length=16, length_penalty=2.0)
-            summary = result[0]['summary']
-
-            # Generate audio
-            speech = gTTS(text=summary, lang="en")
-            speech_bytes = BytesIO()
-            speech.write_to_fp(speech_bytes)
-
-            # Return individual output values
-            return summary, speech_bytes.getvalue(), abstract_text.strip()
-
-    except Exception as e:
-        raise Exception(str(e))
+  try:
+    with open(pdf_file, "rb") as file:
+      pdf_reader = PdfReader(file)
+      abstract_text = ""
+      for page_num in range(len(pdf_reader.pages)):
+        page = pdf_reader.pages[page_num]
+        text = page.extract_text()
+        abstract_match = re.search(r"\bAbstract\b", text, re.IGNORECASE)
+        if abstract_match:
+          start_index = abstract_match.end()
+          introduction_match = re.search(r"\bIntroduction\b", text[start_index:], re.IGNORECASE)
+          if introduction_match:
+            end_index = start_index + introduction_match.start()
+          else:
+            end_index = None
+          abstract_text = text[start_index:end_index]
+          break
+
+      # Summarize the extracted abstract using the LED-large model
+      result = summarizer(abstract_text)
+
+      # Print the entire result for debugging
+      print("Result:", result)
+
+      # Check if 'summary' is present in the result
+      if result and isinstance(result, list) and len(result) > 0:
+        summary = result[0].get('summary', 'Summary not available.')
+      else:
+        summary = "Summary not available."
+
+      # Generate audio
+      speech = gTTS(text=summary, lang="en")
+      speech_bytes = BytesIO()
+      speech.write_to_fp(speech_bytes)
+
+      # Return individual output values
+      return summary, speech_bytes.getvalue(), abstract_text.strip()
+
+  except Exception as e:
+    raise Exception(str(e))
 
 interface = gr.Interface(
-    fn=extract_abstract_and_summarize,
-    inputs=[gr.File(label="Upload PDF")],
-    outputs=[gr.Textbox(label="Summary"), gr.Audio()],
-    title="PDF Summarization & Audio Tool",
-    description="""PDF Summarization App. This app extracts the abstract from a PDF, summarizes it using the 'pszemraj/led-large-book-summary' model, and generates an audio of it. Only upload PDFs with abstracts. Please read the README.MD for information about the app and sample PDFs."""
+  fn=extract_abstract_and_summarize,
+  inputs=[gr.File(label="Upload PDF")],
+  outputs=[gr.Textbox(label="Summary"), gr.Audio()],
+  title="PDF Summarization & Audio Tool",
+  description="""PDF Summarization App. This app extracts the abstract from a PDF, summarizes it using the 'pszemraj/led-large-book-summary' model, and generates an audio of it. Only upload PDFs with abstracts. Please read the README.MD for information about the app and sample PDFs."""
 )
 
 interface.launch()