Update app.py

app.py

@@ -1,40 +1,41 @@
 import fitz
 from transformers import pipeline
+import gradio as gr
 
 def extract_text_from_pdf(pdf_path):
-  doc = fitz.open(pdf_path)
-  text = ""
-  for page_num in range(doc.page_count):
-    page = doc[page_num]
-    text += page.get_text()
-  return text
+    doc = fitz.open(pdf_path)
+    text = ""
+    for page_num in range(doc.page_count):
+        page = doc[page_num]
+        text += page.get_text()
+    return text
 
 def generate_summary(pdf_path):
-  # Load pre-trained Flan-T5 model
-  text_generator = pipeline("text2text-generation", model="google/flan-t5-base")
+    # Load pre-trained Flan-T5 model
+    text_generator = pipeline("text2text-generation", model="google/flan-t5-base")
 
-  # Extract text from the PDF
-  input_text = extract_text_from_pdf(pdf_path)
+    # Extract text from the PDF
+    input_text = extract_text_from_pdf(pdf_path)
 
-  # Generate summary
-  summary = text_generator(input_text, max_length=1024, num_beams=4)
+    # Generate summary
+    summary = text_generator(input_text, max_length=1024, num_beams=4)
 
-  # Return results
-  return {
-    "Extracted Information": input_text,
-    "Book Summary": summary[0]["generated_text"],
-    "Review": "The book conveys a powerful message about..."
-  }
+    # Return results
+    return {
+        "Extracted Information": input_text,
+        "Book Summary": summary[0]["generated_text"],
+        "Review": "The book conveys a powerful message about..."
+    }
 
 iface = gr.Interface(
-  fn=generate_summary,
-  inputs=gr.File(),
-  outputs=[
-    gr.Textbox(),
-    gr.Textbox(),
-    gr.Textbox(),
-  ],
-  live=True
+    fn=generate_summary,
+    inputs=gr.File(),
+    outputs=[
+        gr.Textbox(),
+        gr.Textbox(),
+        gr.Textbox(),
+    ],
+    live=True
 )
 
 iface.launch()