Update app.py

app.py

@@ -1,13 +1,32 @@
-from gradio import Interface, Input, Output, Text
+import gradio as gr 
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from PyPDF2 import PdfReader
+import os
 
-from utils import summarize_and_speak_pdf_abstract
+model = AutoModelForSeq2SeqLM.from_pretrained("ArtifactAI/led_large_16384_arxiv_summarization")
+tokenizer = AutoTokenizer.from_pretrained("ArtifactAI/led_large_16384_arxiv_summarization")
 
-# Define the Gradio interface
-interface = Interface(
-    fn=summarize_and_speak_pdf_abstract,
-    inputs=[Input(type="file", label="Upload PDF")],
-    outputs=[Output(type="text", label="One-sentence Summary")],
-)
+def summarize(pdf):
+    reader = PdfReader(pdf.name)
+    page = next(reader.pages)
+    text = page.extract_text()
 
-# Launch the interface
-interface.launch(title="PDF Abstract Summarizer", description="Summarize the abstract of your PDF in one sentence.")
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model.generate(**inputs)
+    
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
+
+description = """
+         Summarize the abstract from a research paper PDF in one sentence.
+         Works best on papers from ArXiv. Uploaded PDF must contain an abstract section.
+         """
+
+examples = ["paper1.pdf", "paper2.pdf"]
+
+iface = gr.Interface(fn=summarize, inputs="file", outputs="text",
+             examples=examples,
+             title="PDF Abstract Summarizer",
+             description=description)
+
+iface.launch(share=True)