Create app.py

app.py

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+import PyPDF2
+import chromadb
+import streamlit as st
+from langchain_openai import OpenAI
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    text = ""
+    with open(pdf_path, "rb") as file:
+        pdf_reader = PyPDF2.PdfFileReader(file)
+        for page_num in range(pdf_reader.numPages):
+            text += pdf_reader.getPage(page_num).extractText()
+    return text
+
+# Function to create chunks of text
+def create_text_chunks(text, chunk_size=1000, overlap_size=100):
+    chunks = []
+    for i in range(0, len(text), chunk_size - overlap_size):
+        chunks.append(text[i:i + chunk_size])
+    return chunks
+
+# Function to save chunks to chromadb vector database
+def save_to_chromadb(chunks, quiz_name, quiz_topic):
+    # Assume you have a ChromaDB instance named 'db'
+    db = chromadb.ChromaDB("your_chromadb_url")
+    for i, chunk in enumerate(chunks):
+        vector = langchain_openai.get_vector(chunk)
+        db.add_vector(quiz_name, quiz_topic, i, vector)
+
+# Function to generate questions using ChatGPT-3.5-turbo-16k
+def generate_questions(topic):
+    prompt = f"Generate questions on the topic: {topic}"
+    response = langchain_openai.complete(prompt)
+    return response.choices[0].text.strip()
+
+# Streamlit interface
+def main():
+    st.title("Quiz Generator")
+
+    # User inputs
+    quiz_name = st.text_input("Enter Quiz Name:")
+    quiz_topic = st.text_input("Enter Quiz Topic:")
+    num_questions = st.number_input("Number of Questions:", value=5, min_value=1)
+    pdf_path = st.file_uploader("Upload PDF File:", type=["pdf"])
+
+    if pdf_path:
+        # Extract text from PDF
+        pdf_text = extract_text_from_pdf(pdf_path)
+
+        # Create and save text chunks to ChromaDB
+        text_chunks = create_text_chunks(pdf_text)
+        save_to_chromadb(text_chunks, quiz_name, quiz_topic)
+
+        # User input for query
+        user_query = st.text_input("Enter Query for Question Generation:")
+
+        # Search for the topic in the vector database
+        if quiz_topic in db.get_topics(quiz_name):
+            # Generate questions using ChatGPT-3.5-turbo-16k
+            generated_questions = generate_questions(user_query)
+
+            st.subheader("Generated Questions:")
+            st.write(generated_questions)
+        else:
+            st.warning("Specified topic not found in the document.")
+
+if __name__ == "__main__":
+    main()