Update app.py

app.py

@@ -1,14 +1,71 @@
+import os
 import streamlit as st
 from PyPDF2 import PdfReader
 from sentence_transformers import SentenceTransformer
 import faiss
-import os
+import numpy as np
 from groq import Groq
 
-# Initialize Groq client
+# Initialize Groq Client
 GROQ_API_KEY = os.environ.get('GroqApi')
 client = Groq(api_key=GROQ_API_KEY)
 
+# Initialize Embedding Model
+embedding_model = SentenceTransformer('distilbert-base-uncased')
+
+# Streamlit UI
+st.title("RAG-based Quiz App")
+
+# File Upload
+uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
+if uploaded_file is not None:
+    # Extract Text from PDF
+    pdf_reader = PdfReader(uploaded_file)
+    text = " ".join([page.extract_text() for page in pdf_reader.pages])
+    
+    # Chunking Text
+    st.write("Processing the PDF...")
+    chunks = [text[i:i+500] for i in range(0, len(text), 500)]
+    
+    # Create Embeddings
+    embeddings = embedding_model.encode(chunks)
+    embeddings = np.array(embeddings, dtype="float32")
+    
+    # FAISS Index
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings)
+    
+    st.success("PDF Processed! Embeddings Created.")
+    
+    # Generate Questions
+    st.write("Generating Quiz Questions...")
+    questions = []
+    for chunk in chunks[:5]:  # Generate questions for the first few chunks
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": f"Create a multiple-choice quiz question from this text: {chunk}"}],
+            model="llama3-8b-8192"
+        )
+        question = response.choices[0].message.content
+        questions.append(question)
+    
+    st.success("Quiz Questions Generated!")
+    
+    # Display Quiz
+    for idx, question in enumerate(questions):
+        st.write(f"**Question {idx+1}:** {question}")
+        options = ["Option A", "Option B", "Option C", "Option D"]  # Placeholder
+        selected_option = st.radio(f"Select your answer for Question {idx+1}", options, key=idx)
+        if st.button(f"Submit Answer for Question {idx+1}", key=f"submit_{idx}"):
+            # Dummy Logic: Assume Option A is correct for demonstration
+            correct_option = "Option A"
+            if selected_option == correct_option:
+                st.success("Correct Answer!")
+            else:
+                st.error(f"Wrong Answer! Correct Answer: {correct_option}")
+
+# Footer
+st.write("App developed and deployed using Hugging Face Spaces.")
+
 # Initialize embedding model
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')