Update app.py

app.py

@@ -1,48 +1,40 @@
 import fitz
-from transformers import GPT2Tokenizer, GPT2Model
-import gradio as gr
+from transformers import pipeline
 
 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
-
-def generate_review(summary):
-    # Your custom logic to generate a review
-    review = "The book conveys a powerful message about..."
-    return review
+  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 GPT-2 model and tokenizer
-    tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-    model = GPT2Model.from_pretrained('gpt2')
+  # 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)
 
-    # Tokenize and generate summary
-    input_tokens = tokenizer.encode(input_text, return_tensors="pt")
-    summary = model.generate(input_tokens)
+  # Generate summary
+  summary = text_generator(input_text, max_length=1024, num_beams=4)
 
-    # Return results
-    return {
-        "Extracted Information": input_text,
-        "Book Summary": tokenizer.decode(summary[0], skip_special_tokens=True),
-        "Review": generate_review(summary)
-    }
+  # 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()