Update app.py

app.py

@@ -1,24 +1,39 @@
 import gradio as gr
 from transformers import pipeline
+import fitz  # PyMuPDF
 
 # Load summarization pipeline
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Define function to summarize input text
-def summarize_notes(text):
-    if len(text.strip()) == 0:
-        return "Please paste your academic notes."
-    summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
-    return summary[0]['summary_text']
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    doc = fitz.open(stream=pdf_file.read(), filetype="pdf")
+    text = ""
+    for page in doc:
+        text += page.get_text()
+    return text
 
-# Gradio interface
+# Combine everything into one function
+def summarize_pdf(pdf_file):
+    try:
+        text = extract_text_from_pdf(pdf_file)
+        if len(text.strip()) == 0:
+            return "The PDF seems empty or text is not extractable."
+        # Truncate long text (BART model has ~1024 token limit)
+        text = text[:3000]
+        summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
+        return summary[0]['summary_text']
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio Interface
 demo = gr.Interface(
-    fn=summarize_notes,
-    inputs=gr.Textbox(lines=15, placeholder="Paste your academic notes here...", label="Academic Notes"),
+    fn=summarize_pdf,
+    inputs=gr.File(label="Upload PDF of Academic Notes"),
     outputs=gr.Textbox(label="Summarized Notes"),
-    title="📚 Academic Note Summarizer",
-    description="Paste your long academic notes and get a short summary using a Hugging Face Transformer model."
+    title="📄 Academic Note Summarizer (PDF)",
+    description="Upload your academic notes in PDF format. The app will extract and summarize the content using a Hugging Face model."
 )
 
-# Launch the app
 demo.launch()
+