Update app.py

app.py

@@ -1,103 +1,104 @@
 # https://elrmnd-vocal-pdf-summarizer.hf.space
 
-# Import  libraries
+# Import libraries
 
 import gradio as gr
 import PyPDF2
-import torch
-from transformers import pipeline 
-import scipy
-import numpy
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 from gtts import gTTS
 from io import BytesIO
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Function to extract text from PDF 
+# Function to extract text from PDF
 # Defines a function to extract raw text from a PDF file
 def extract_text(pdf_file):
     pdfReader = PyPDF2.PdfReader(pdf_file)
     pageObj = pdfReader.pages[0]
     return pageObj.extract_text()
 
-    
 # Function to summarize text
-# Defines a function to summarize the extracted text using facebook/bart-large-cnn    
+# Defines a function to summarize the extracted text using facebook/bart-large-cnn
 def summarize_text(text):
     sentences = text.split(". ")
+    start = -1  # Default value if "Abstract" is not found
+    end = -1
+
     for i, sentence in enumerate(sentences):
         if "Abstract" in sentence:
             start = i + 1
             end = start + 6
             break
-    abstract = ". ".join(sentences[start:end+1]) 
-
-  # Load BART model & tokenizer
-    tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary")
-    model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary")
-
-    # Tokenize abstract 
-    inputs = tokenizer(abstract,
-                       max_length=1024,
-                       return_tensors="pt",
-                       truncation=True)
-
-    # Generate summary
-    summary_ids = model.generate(inputs['input_ids'],
-                                 max_length=50,
-                                 min_length=30,
-                                 no_repeat_ngram_size=3,
-                                 encoder_no_repeat_ngram_size=3,
-                                 repetition_penalty=3.5,
-                                 num_beams=4,
-                                 do_sample=True,
-                                 early_stopping=False)
-    
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-    if '.' in summary:
-        index = summary.rindex('.')
-        if index != -1:
-           summary = summary[:index+1]
+
+    if start != -1:
+        abstract = ". ".join(sentences[start:end + 1])
+
+        # Load BART model & tokenizer
+        tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary")
+        model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary")
+
+        # Tokenize abstract
+        inputs = tokenizer(abstract,
+                           max_length=1024,
+                           return_tensors="pt",
+                           truncation=True)
+
+        # Generate summary
+        summary_ids = model.generate(inputs['input_ids'],
+                                     max_length=50,
+                                     min_length=30,
+                                     no_repeat_ngram_size=3,
+                                     encoder_no_repeat_ngram_size=3,
+                                     repetition_penalty=3.5,
+                                     num_beams=4,
+                                     do_sample=True,
+                                     early_stopping=False)
+
+        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+        if '.' in summary:
+            index = summary.rindex('.')
+            if index != -1:
+                summary = summary[:index + 1]
+    else:
+        summary = "Abstract not found in the document."
 
     return summary
 
 # Function to convert text to audio
 # Defines a function to convert text to an audio file using Google Text-to-Speech
 def text_to_audio(text):
-    tts = gTTS(text, lang='en') 
+    tts = gTTS(text, lang='en')
     buffer = BytesIO()
     tts.write_to_fp(buffer)
-    buffer.seek(0)  
+    buffer.seek(0)
     return buffer.read()
 
-### Main function 
+### Main function
 ### The main function that ties everything together:
 ### extracts text, summarizes, and converts to audio.
 def audio_pdf(pdf_file):
     text = extract_text(pdf_file)
-    summary = summarize_text(text) 
+    summary = summarize_text(text)
     audio = text_to_audio(summary)
     return summary, audio
 
 # Define Gradio interface
 # Gradio web interface with a file input, text output to display the summary
 # and audio output to play the audio file. # Launches the interface
-inputs = gr.File() 
+inputs = gr.File()
 summary_text = gr.Text()
 audio_summary = gr.Audio()
 
-
 iface = gr.Interface(
     fn=audio_pdf,
     inputs=inputs,
-    outputs=[summary_text,audio_summary],
+    outputs=[summary_text, audio_summary],
     title="The Vocal PDF Summarizer",
-    description="I will summarize your pdf and transform it in to an audio",
+    description="I will summarize your pdf and transform it into audio",
     examples=["Article 11 Hidden Technical Debt in Machine Learning Systems.pdf",
-          "Article 6 BloombergGPT_ A Large Language Model for Finance.pdf",
-          "Article 5 A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks.pdf",
-          "Article 8 Llama 2_ Open Foundation and Fine-Tuned Chat Models.pdf"
-         ]
+              "Article 6 BloombergGPT_ A Large Language Model for Finance.pdf",
+              "Article 5 A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks.pdf",
+              "Article 8 Llama 2_ Open Foundation and Fine-Tuned Chat Models.pdf"
+             ]
 )
 
-iface.launch() # Launch the interface
+iface.launch()  # Launch the interface