Update app.py

app.py

@@ -2,88 +2,81 @@ import gradio as gr
 from transformers import pipeline
 from tempfile import NamedTemporaryFile
 from PyPDF2 import PdfReader
-from IPython.display import Audio
-import numpy as np
 from bark import SAMPLE_RATE, generate_audio, preload_models
 from scipy.io.wavfile import write as write_wav
 import torch
 
-def summarize_abstract_from_pdf(pdf_file_path):
-    # Initialize the summarization pipeline
+# Initialize the summarization pipeline
+summarizer = pipeline("summarization", "pszemraj/led-base-book-summary", device=0 if torch.cuda.is_available() else -1)
 
+# Function to summarize abstract from PDF
+def summarize_abstract_from_pdf(pdf_file_path):
     abstract_string = 'abstract'
+    intro_string = 'introduction'
     found_abstract = False
-    intro_string ='introduction'
-    extracted_text_string =""
+    extracted_text_string = ""
 
     # Read the PDF and extract text from the first page
     with open(pdf_file_path, 'rb') as pdf_file:
         reader = PdfReader(pdf_file)
-        text = ""
-        text += reader.pages[0].extract_text()
-
+        text = reader.pages[0].extract_text()
 
     file = text.splitlines()
     for lines in file:
-      lower_lines = lines.lower()
-      if lower_lines.strip()== abstract_string:
-        found_abstract = True
-      elif "1" in lower_lines.strip() and intro_string in lower_lines.strip():
-        found_abstract = False
-
-      if found_abstract == True:
-        extracted_text_string += lines
+        lower_lines = lines.lower()
+        if lower_lines.strip() == abstract_string:
+            found_abstract = True
+        elif "1" in lower_lines.strip() and intro_string in lower_lines.strip():
+            found_abstract = False
 
+        if found_abstract:
+            extracted_text_string += lines
 
     extracted_text_string = extracted_text_string.replace("Abstract", "")
-    summarizer = pipeline("summarization", "pszemraj/led-base-book-summary",device=0 if torch.cuda.is_available() else -1,)
+
     # Generate a summarized abstract using the specified model
-    summarized_abstract = summarizer(extracted_text_string,
-    min_length=16,
-    max_length=150,
-    no_repeat_ngram_size=3,
-    encoder_no_repeat_ngram_size=3,
-    repetition_penalty=3.5,
-    num_beams=4,
-    early_stopping=True,
-    )
-    #I run this twice to get summazired text
-    summarized_abstract2 = summarizer(summarized_abstract[0]['summary_text'],
-    min_length=16,
-    max_length=25,
-    no_repeat_ngram_size=3,
-    encoder_no_repeat_ngram_size=3,
-    repetition_penalty=3.5,
-    num_beams=4,
-    early_stopping=True,
+    summarized_abstract = summarizer(
+        extracted_text_string,
+        min_length=16,
+        max_length=150,
+        no_repeat_ngram_size=3,
+        encoder_no_repeat_ngram_size=3,
+        repetition_penalty=3.5,
+        num_beams=4,
+        early_stopping=True,
     )
 
-
+    # Run summarization twice to get the summarized text
+    summarized_abstract2 = summarizer(
+        summarized_abstract[0]['summary_text'],
+        min_length=16,
+        max_length=25,
+        no_repeat_ngram_size=3,
+        encoder_no_repeat_ngram_size=3,
+        repetition_penalty=3.5,
+        num_beams=4,
+        early_stopping=True,
+    )
 
     # Return the summarized abstract as a string
     return summarized_abstract2[0]['summary_text']
 
+# Function to generate audio from PDF
 def generate_audio_func(pdf_file):
-
-
-  model_name = "suno/bark-small"
-  # Download and load the specified model
-  preload_models(model_name)
+    model_name = "suno/bark-small"
+    
+    # Download and load the specified model
+    preload_models(model_name)
+    
     # Access the input file path
-  pdf_file_path = pdf_file.name
-
-  # Generate audio from text
-  #call the summarize abstract function
-  text_prompt =  summarize_abstract_from_pdf(pdf_file_path)
-  audio_array = generate_audio(text_prompt)
-  # Create a temporary WAV file to save the audio
-  with NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav_file:
-    wav_file_path = temp_wav_file.name
-    write_wav(wav_file_path, 22050, (audio_array * 32767).astype(np.int16))
-
-  return wav_file_path
-
+    pdf_file_path = pdf_file.name
+    
+    # Generate audio from text
+    # Call the summarize abstract function
+    text_prompt = summarize_abstract_from_pdf(pdf_file_path)
+    audio_array = generate_audio(text_prompt)
 
+    return audio_array  # Return the audio data as a NumPy array
 
 # Define app name, app description, and examples
 app_name = "PDF to Audio Converter"
@@ -101,4 +94,4 @@ demo = gr.Interface(
     description=app_description
 )
 
-demo.launch()
+demo.launch()