Update app.py

app.py

@@ -1,82 +1,87 @@
-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
 
-# 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):
+    
+    # Initialize the summarization pipeline
+
     abstract_string = 'abstract'
-    intro_string = 'introduction'
     found_abstract = False
-    extracted_text_string = ""
+    intro_string ='introduction'
+    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 = reader.pages[0].extract_text()
+        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
+      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
+      if found_abstract == True:
+        extracted_text_string += lines
 
-    extracted_text_string = extracted_text_string.replace("Abstract", "")
 
+    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,
+    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,
+    #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,
     )
 
+
+
     # 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
+  # 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)
+
+  # 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
+
 
-    return audio_array  # Return the audio data as a NumPy array
 
 # Define app name, app description, and examples
 app_name = "PDF to Audio Converter"
@@ -94,4 +99,4 @@ demo = gr.Interface(
     description=app_description
 )
 
-demo.launch()
+demo.launch()