Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import pipeline
+import PyPDF2
+
+# Load the summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def summarize_pdf(pdf_file):
+    try:
+        # Extract text from the uploaded PDF
+        pdf_reader = PyPDF2.PdfReader(pdf_file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+        
+        # Check if text was extracted
+        if not text.strip():
+            return "❌ Could not extract text from the PDF. Please upload a valid document."
+
+        # Summarize the extracted text
+        summary = summarizer(text, max_length=300, min_length=50, do_sample=False)
+        return summary[0]['summary_text']
+
+    except Exception as e:
+        return f"❌ An error occurred: {str(e)}"
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=summarize_pdf,
+    inputs=gr.inputs.File(label="Upload PDF"),
+    outputs=gr.outputs.Textbox(label="Summary"),
+    title="PDF Summarizer",
+    description="Upload a PDF file to extract and summarize its content using state-of-the-art AI."
+)
+
+interface.launch()