Update app.py

app.py

@@ -10,65 +10,60 @@ model_name = "pszemraj/led-base-book-summary"
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+
 def extract_first_sentence(text):
-    sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
+    """
+    Extracts the first sentence from a given text.
+    """
+    sentences = re.split(r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s+", text)
     if sentences:
         return sentences[0]
     else:
         return text
 
+
 def extract_abstract_and_summarize(pdf_file):
+    """
+    Extracts the abstract and summarizes it in one sentence with information till "Introduction".
+    """
     try:
-        with open(pdf_file, 'rb') as file:
+        with open(pdf_file, "rb") as file:
             pdf_reader = PdfReader(file)
-            abstract_text = ''
-
+            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)
-
+                abstract_match = re.search(r"\bAbstract\b", text, re.IGNORECASE)
                 if abstract_match:
                     start_index = abstract_match.end()
-
-                    # Check for the next heading or section marker
-                    next_section_match = re.search(r'\b(?:Introduction|Methodology|Conclusion)\b', text[start_index:])
-
-                    if next_section_match:
-                        end_index = start_index + next_section_match.start()
-                        abstract_text = text[start_index:end_index]
+                    introduction_match = re.search(r"\bIntroduction\b", text[start_index:], re.IGNORECASE)
+                    if introduction_match:
+                        end_index = start_index + introduction_match.start()
                     else:
-                        abstract_text = text[start_index:]
-
+                        end_index = None
+                    abstract_text = text[start_index:end_index]
                     break  # Exit loop once abstract is found
-
             # Summarize the extracted abstract
             inputs = tokenizer(abstract_text, return_tensors="pt")
             outputs = model.generate(**inputs)
             summary = tokenizer.decode(outputs[0])
-
             # Extract only the first sentence
             summary_sentence = extract_first_sentence(summary)
-
             # Generate audio
             speech = gTTS(text=summary_sentence, lang="en")
             speech_bytes = BytesIO()
             speech.write_to_fp(speech_bytes)
-
             # Return individual output values
             return summary_sentence, 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 in one sentence, and generates an audio of it. Only upload PDFs with abstracts. 
-    Please read the README.MD for information about the app and sample PDFs.""",
+    description="""PDF Summarization App. This app extracts the abstract from a PDF, summarizes it in one sentence with information till "Introduction", 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(share=True)
+interface.launch(share=True)