Upload 3 files

app (1).py

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+import gradio as gr
+from PyPDF2 import PdfReader
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from gtts import gTTS
+from io import BytesIO
+import re
+
+model_name = "ArtifactAI/led_large_16384_arxiv_summarization"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def extract_first_sentence(text):
+    sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
+    if sentences:
+        return sentences[0]
+    else:
+        return text
+
+def summarize_pdf_abstract(pdf_file):
+    try:
+        reader = PdfReader(pdf_file)
+        abstract_text = ""
+        for page in reader.pages:
+            if "Abstract" in page.extract_text() or "Introduction" in page.extract_text():
+                abstract_text = page.extract_text()
+                break
+
+        inputs = tokenizer(abstract_text, return_tensors="pt")
+        outputs = model.generate(**inputs)
+        summary = tokenizer.decode(outputs[0])
+
+        # Extract only the first sentence
+        summary_sentence = extract_first_sentence(summary)
+
+        # Generate audio
+        speech = gTTS(text=summary_sentence, lang="en")
+        speech_bytes = BytesIO()
+        speech.write_to_fp(speech_bytes)
+
+        # Return individual output values
+        return summary_sentence, speech_bytes.getvalue()
+
+    except Exception as e:
+        raise Exception(str(e))
+
+interface = gr.Interface(
+    fn=summarize_pdf_abstract,
+    inputs=[gr.File(label="Upload PDF")],
+    outputs=[gr.Textbox(label="Summary"), gr.Audio()],
+)
+
+interface.launch(share=True)

requirements (1).txt

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+gradio
+transformers
+PyPDF2
+gtts
+torch
+numpy
+pytest
+sphinx
+huggingface-hub
+IPython 
+torchvision 
+torchaudio
+tensorflow
+flax