Create app.py

app.py

@@ -0,0 +1,61 @@
+import gradio as gr
+import torch
+import PyPDF2
+from transformers import pipeline 
+import numpy
+import scipy
+from gtts import gTTS
+from io import BytesIO
+from transformers import BartTokenizer
+
+def extract_text(pdf_file):
+    pdfReader = PyPDF2.PdfReader(pdf_file)
+    pageObj = pdfReader.pages[0]
+    return pageObj.extract_text()
+
+
+def summarize_text(text):
+    sentences = text.split(". ")
+    for i, sentence in enumerate(sentences):
+        if "Abstract" in sentence:
+            start = i + 1
+            end = start + 3
+            break
+    abstract = ". ".join(sentences[start:end+1]) 
+
+    tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
+    summarizer = pipeline("summarization", model="facebook/bart-large-cnn", tokenizer=tokenizer) 
+    summary = summarizer(abstract, max_length=30, min_length=30,
+                         do_sample=False)
+    return summary[0]['summary_text']
+
+def text_to_audio(text):
+    tts = gTTS(text, lang='en') 
+    buffer = BytesIO()
+    tts.write_to_fp(buffer)
+    buffer.seek(0)  
+    return buffer.read()
+
+def audio_pdf(pdf_file):
+    text = extract_text(pdf_file)
+    summary = summarize_text(text) 
+    audio = text_to_audio(summary)
+    return summary, audio
+
+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="PDF Audio Summarizer 📻",
+    description="App that converts an abstract into audio",
+    examples=["Attention_is_all_you_need.pdf", 
+              "ImageNet_Classification.pdf"
+             ]
+)
+
+iface.launch()