Create app.py

app.py

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+# Import  libraries
+
+import gradio as gr
+import PyPDF2
+import torch
+from transformers import pipeline 
+import scipy
+import numpy
+from gtts import gTTS
+from io import BytesIO
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# 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(". ")
+    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]
+
+    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()
+
+### 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()
+
+
+iface = gr.Interface(
+    fn=audio_pdf,
+    inputs=inputs,
+    outputs=[summary_text,audio_summary],
+    title="The Vocal PDF Summarizer",
+    description="I will summarize your pdf and transform it in to an audio",
+    examples=["Article 11 Hidden Technical Debt in Machine Learning Systems.pdf"
+             ]
+)
+
+iface.launch() # Launch the interface