Create app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from sentence_transformers import SentenceTransformer
+import faiss
+import os
+from groq import Groq
+
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Initialize embedding model
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Upload PDF
+st.title("PDF to Quiz Generator")
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+
+if uploaded_file:
+    # Extract text from PDF
+    st.write("Processing PDF...")
+    reader = PdfReader(uploaded_file)
+    pdf_text = ""
+    for page in reader.pages:
+        pdf_text += page.extract_text()
+
+    # Split text into chunks
+    chunk_size = 512  # Adjust as needed
+    text_chunks = [pdf_text[i:i + chunk_size] for i in range(0, len(pdf_text), chunk_size)]
+
+    # Generate embeddings
+    st.write("Generating embeddings...")
+    embeddings = embedding_model.encode(text_chunks)
+
+    # Store embeddings in FAISS
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(embeddings)
+
+    # Generate questions using Groq API
+    def generate_question(content):
+        response = client.chat.completions.create(
+            messages=[{"role": "user", "content": f"Generate a multiple-choice question from: {content}"}],
+            model="llama3-8b-8192",
+        )
+        return response.choices[0].message.content
+
+    # Generate quiz
+    st.write("Generating quiz...")
+    quiz = []
+    for chunk in text_chunks:
+        question = generate_question(chunk)
+        quiz.append(question)
+
+    # Display the quiz
+    st.write("Here is your quiz:")
+    for i, q in enumerate(quiz, 1):
+        st.markdown(f"**Question {i}:** {q}")
+
+---