Update app.py

app.py

@@ -1,10 +1,9 @@
 import streamlit as st
 import fitz  # PyMuPDF
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
 # Load model directly
 model_name = "openai-community/gpt2"
-# Load the LLaMA model and tokenizer
-#model_name = "meta-llama/Meta-Llama-Guard-2-8B"  # Update this with the correct LLaMA model name
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
@@ -17,7 +16,7 @@ def extract_text_from_pdf(pdf_file):
         text += page.get_text()
     return text
 
-# Function to generate MCQs using the LLaMA model
+# Function to generate MCQs using the model
 def generate_mcqs(text, num_questions=5):
     if not text.strip():
         return ["No text extracted from the PDF. Unable to generate MCQs."]
@@ -30,15 +29,15 @@ def generate_mcqs(text, num_questions=5):
     mcqs = []
     for _ in range(num_questions):
         # Generate a single MCQ at a time
-        input_text = f"Generate a multiple choice question from the following text: {tokenizer.decode(inputs['input_ids'][0])}"
+        input_text = f"Based on the following text, generate a multiple choice question:\n\n{text}\n\nQuestion:"
         generated = generator(input_text, max_length=400, num_return_sequences=1)
-        question_text = generated[0]["generated_text"]
+        question_text = generated[0]["generated_text"].split("Question:")[1].strip()
         
         # Format the MCQ
-        options = ["Option A: ABC", "Option B: DEF", "Option C: GHI", "Option D: JKL"]  # Placeholder options
-        correct_answer = "Option A: ABC"  # Placeholder correct answer for now
+        options = ["Option A: Placeholder A", "Option B: Placeholder B", "Option C: Placeholder C", "Option D: Placeholder D"]
+        correct_answer = "Option A: Placeholder A"  # Placeholder correct answer for now
         
-        mcq_formatted = f"Q: {question_text}\nOption A: {options[0]}\nOption B: {options[1]}\nOption C: {options[2]}\nOption D: {options[3]}\nCorrect Answer: {correct_answer}"
+        mcq_formatted = f"Q: {question_text}\n{options[0]}\n{options[1]}\n{options[2]}\n{options[3]}\nCorrect Answer: {correct_answer}"
         mcqs.append(mcq_formatted)
 
     return mcqs