Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import BartTokenizer, BartForConditionalGeneration
+import PyPDF2
+
+# Load BART model and tokenizer
+model_name = "facebook/bart-large-cnn"
+tokenizer = BartTokenizer.from_pretrained(model_name)
+model = BartForConditionalGeneration.from_pretrained(model_name)
+
+# Function to summarize PDF text
+def summarize_pdf(pdf_file):
+    # Read the PDF file
+    pdf_reader = PyPDF2.PdfReader(pdf_file.name)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+
+    # Summarize the text
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
+    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Interface for Gradio
+iface = gr.Interface(
+    fn=summarize_pdf,
+    inputs="file",
+    outputs="text",
+    title="PDF Summarizer",
+    description="Upload a PDF file, and the model will provide a summary.",
+    live=True,
+    capture_session=True,
+)
+
+# Launch the Gradio interface
+iface.launch()