Create app.py

app.py

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+import gradio as gr
+from pathlib import Path
+from pypdf import PdfReader
+
+from transformers.utils import logging
+
+logging.set_verbosity_error()
+
+from transformers import pipeline
+import torch
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+summarizer = pipeline(
+    task="summarization",
+    model="facebook/bart-large-cnn",
+    torch_dtype=torch.bfloat16,
+    device=device
+)
+
+narrator = pipeline("text-to-speech", model="suno/bark-small", device=device)
+
+
+def upload_file(filepath):
+    file_path = Path(filepath).name
+    return file_path
+
+
+def pdf_to_audio(file_output):
+    pdf_summary = ""
+    pdf_reader = PdfReader(file_output)
+    for page in pdf_reader.pages[:1]:
+        page_summary = summarizer(page.extract_text(), min_length=10, max_length=10)
+        pdf_summary = pdf_summary + page_summary[0]["summary_text"] + " "
+    narrated_text = narrator(pdf_summary)
+    return gr.Audio((narrated_text["sampling_rate"], narrated_text["audio"].T))
+
+
+with gr.Blocks() as demo:
+    file_output = gr.File()
+    upload_button = gr.UploadButton(
+        "Click to upload your PDF file", file_types=["file"], file_count="single"
+    )
+    upload_button.upload(upload_file, upload_button, file_output)
+
+    audio = gr.Interface(
+        fn=pdf_to_audio,
+        inputs=file_output,
+        outputs="audio",
+    )
+
+if __name__ == "__main__":
+    demo.launch(debug=True)