Update app.py

app.py

@@ -1,32 +1,28 @@
-import gradio as gr 
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+# app/main.py
 from PyPDF2 import PdfReader
-import os
-
-model = AutoModelForSeq2SeqLM.from_pretrained("ArtifactAI/led_large_16384_arxiv_summarization")
-tokenizer = AutoTokenizer.from_pretrained("ArtifactAI/led_large_16384_arxiv_summarization")
-
-def summarize(pdf):
-    reader = PdfReader(pdf.name)
-    page = next(reader.pages)
-    text = page.extract_text()
-
-    inputs = tokenizer(text, return_tensors="pt")
-    outputs = model.generate(**inputs)
-    
-    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return summary
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from gtts import gTTS
+import gradio as gr
 
-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.
-         """
+# Load the pre-trained model and tokenizer
+model_name = "ArtifactAI/led_large_16384_arxiv_summarization"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-examples = ["paper1.pdf", "paper2.pdf"]
+def summarize_pdf_abstract(pdf_path):
+    # Implement the function to summarize PDF abstracts (similar to your previous code)
+    # ...
 
-iface = gr.Interface(fn=summarize, inputs="file", outputs="text",
-             examples=examples,
-             title="PDF Abstract Summarizer",
-             description=description)
+# Gradio Interface
+iface = gr.Interface(
+    fn=summarize_pdf_abstract,
+    inputs=gr.File(type="file", label="Upload a PDF file"),
+    outputs="text",
+    live=True,
+    interpretation="default",
+    title="PDF Abstract Summarizer",
+    description="This app accepts PDFs with abstracts and generates a summary.",
+)
 
-iface.launch(share=True)
+# Launch the Gradio interface
+iface.launch()