Create app.py

app.py

@@ -0,0 +1,36 @@
+import fitz  # PyMuPDF
+import gradio as gr
+from transformers import pipeline
+
+# Initialize summarizer pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def extract_text_from_pdf(file):
+    doc = fitz.open(stream=file.read(), filetype="pdf")
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
+
+def summarize_pdf(file):
+    raw_text = extract_text_from_pdf(file)
+    # Limit to avoid token overflow
+    max_chunk = 1024
+    chunks = [raw_text[i:i+max_chunk] for i in range(0, len(raw_text), max_chunk)]
+    summary = ""
+    for chunk in chunks:
+        res = summarizer(chunk, max_length=130, min_length=30, do_sample=False)
+        summary += res[0]['summary_text'] + " "
+    return summary.strip()
+
+# Gradio UI
+demo = gr.Interface(
+    fn=summarize_pdf,
+    inputs=gr.File(label="Upload a PDF"),
+    outputs=gr.Textbox(label="Summary"),
+    title="📄 PDF Summarizer",
+    description="Upload a PDF file and get an AI-generated summary using Hugging Face Transformers."
+)
+
+if __name__ == "__main__":
+    demo.launch()