Create app.py

app.py

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+# https://huggingface.co/spaces/itsmariamaraki/AAI-Assessment3
+
+# Here are the imports
+
+import gradio as gr
+import PyPDF2
+from PyPDF2 import PdfReader
+from pdfminer.high_level import extract_pages, extract_text
+from transformers import pipeline, AutoProcessor, AutoModel, AutoTokenizer
+import torch
+import soundfile as sf
+from IPython.display import Audio
+from datasets import load_dataset
+from io import BytesIO
+
+# Here is the code
+
+def abstract(pdf_file):
+    pdf_bytes = BytesIO(pdf_file)
+    pdf_reader = PyPDF2.PdfReader(pdf_bytes)
+    
+    abstract = ''
+    
+    for page_number in range(len(pdf_reader.pages)):
+        text = pdf_reader.pages[page_number].extract_text()
+        
+        if 'abstract' in text.lower(): #in order to read only the abstract, i set as a start the abstract point & as an end the introduction point
+            start_index = text.lower().find('abstract')  
+            end_index = text.lower().find('introduction')
+            abstract = text[start_index:end_index]
+            break
+    
+    return abstract
+
+
+
+summarization = pipeline('summarization', model = 'pszemraj/long-t5-tglobal-base-16384-book-summary') #best summarization model i tested regarding this assessment
+audiospeech = pipeline('text-to-speech', model = 'suno/bark-small') #the voice is a bit distorted but gives a good output & takes less time
+
+
+
+def summarization_n_audiospeech(pdf_file):
+    abstract_text = abstract(pdf_file)
+
+    summary = summarization(abstract_text, max_length=50, min_length=10)[0]['summary_text'] #didn't know exactly what would give one sentence, so i changed multiple type the min & max lengths. for a dif article, those parameters would have to be different as well
+
+    #converting the summarization into an audio output
+    tts_output = audiospeech(summary)
+    audio_data = tts_output['audio'][0]
+    
+    with BytesIO() as buffer:
+        sf.write(buffer, audio_data, 16000, format = 'wav')
+        audio_bytes = buffer.getvalue()
+
+    return summary, audio_bytes
+
+
+
+iface = gr.Interface(
+    fn = summarization_n_audiospeech,
+    inputs = gr.File(label='upload PDF', type='binary'), #if i didn't set a type, the gradio output was an error -  searched it online for the solution
+    outputs = [
+        gr.Textbox(label='Summarization of the Abstract:'), 
+        gr.Audio(label="Audio Speech of the Abstract's Summary:")
+        ],
+    live = True
+)
+
+iface.launch()