Update app.py

app.py

@@ -1,3 +1,5 @@
+# Import  libraries
+
 import gradio as gr
 import torch
 import PyPDF2
@@ -7,27 +9,34 @@ import scipy
 from gtts import gTTS
 from io import BytesIO
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from bark import SAMPLE_RATE, generate_audio, preload_models
 
 
+# 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    
 def summarize_text(text):
     sentences = text.split(". ")
-    
-    # Find abstract section
     for i, sentence in enumerate(sentences):
         if "Abstract" in sentence:
             start = i + 1
             end = start + 6
             break
             
-    # Extract abstract 
-    abstract = ". ".join(sentences[start:end+1])
+    if start is not None and end is not None:  
+        abstract = ". ".join(sentences[start:end+1])
+        #print(abstract)
+    else:                                            #if the Abstract is not found
+        return("Abstract section not found")
 
-    # Load BART model & tokenizer
+  # Load BART model & tokenizer
     tokenizer = AutoTokenizer.from_pretrained("pszemraj/led-base-book-summary")
     model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/led-base-book-summary")
 
@@ -39,13 +48,17 @@ def summarize_text(text):
 
     # Generate summary
     summary_ids = model.generate(inputs['input_ids'],
-                                 num_beams=3,
-                                 max_length=30,
-                                 min_length=21,
-                                 do_sample=False,
-                                 early_stopping=True)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+                                 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:
@@ -53,25 +66,33 @@ def summarize_text(text):
 
     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') 
     #buffer = BytesIO()
     #tts.write_to_fp(buffer)
     #buffer.seek(0)  
     #return buffer.read()
+#######################
+    preload_models()
+    tts = generate_audio(summary)
+    return (SAMPLE_RATE, tts)
 
-    synthesiser = pipeline("text-to-speech", "suno/bark")
-
-    speech = synthesiser[str("summary")]
 
-    scipy.io.wavfile.write("speech.wav", rate=speech["sampling_rate"], data=speech["audio"])
 
+### 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) 
     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() 
 summary_text = gr.Text()
 audio_summary = gr.Audio()
@@ -83,9 +104,10 @@ iface = gr.Interface(
     outputs=[summary_text,audio_summary],
     title="PDF Audio Summarizer 📻",
     description="App that converts an abstract into audio",
-    examples=["Attention_is_all_you_need.pdf", 
+    examples=["Hidden_Technical_Debt.pdf",
+              "Attention_is_all_you_need.pdf", 
               "ImageNet_Classification.pdf"
              ]
 )
 
-iface.launch()
+iface.launch() # Launch the interface