Create app.py

app.py

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+import gradio as gr
+
+from transformers import pipeline
+import PyPDF2
+import pdfplumber 
+
+import torch
+
+import soundfile as sf
+
+from IPython.display import Audio
+
+from datasets import load_dataset
+
+from pdfminer.high_level import extract_pages, extract_text
+
+summarization = pipeline ('summarization', model = "pszemraj/long-t5-tglobal-base-16384-book-summary")
+
+def summarize_and_speech(pdf_file):
+    with open(pdf_file.name, 'rb') as file:
+        pdf_reader = PyPDF2.PdfFileReader(file)
+        abstract_text = pdf_reader.pages[0].extract_text()
+        summary = summarization(abstract_text, max_length=13, min_length=10)[0]['summary_text']
+        
+        # Use a text-to-speech model to generate audio
+        synthesiser = pipeline("text-to-speech", "facebook/mms-tts-eng")
+        tts_output = synthesiser(summary)
+        audio_data = tts_output[0]["audio"]
+
+        return summary, audio_data
+
+iface = gr.Interface(
+    fn= summarize_and_speech,
+    inputs=gr.File(label="Upload PDF", type="binary"),
+    outputs=[gr.Textbox(label="Abstract Summary:"), gr.Audio(type="filepath", label="Summary_Speech")],
+    live=True,
+    title="Abstract_Research_Paper_Summarizer",
+    description="Upload a Research Paper PDF File. The model will generate a one line summary of the Abstract section and a speech audio."
+)
+
+iface.launch()
+