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import streamlit as st
import PyPDF2
import openai
import faiss
import os
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from io import BytesIO
# Function to extract text from a PDF file
def extract_text_from_pdf(pdf_file):
reader = PyPDF2.PdfReader(pdf_file)
text = ""
for page in reader.pages:
text += page.extract_text()
return text
# Function to generate embeddings for a piece of text
def get_embeddings(text, model="text-embedding-ada-002"):
response = openai.Embedding.create(input=[text], model=model)
return response['data'][0]['embedding']
# Function to search for similar content
def search_similar(query_embedding, index, stored_texts, top_k=3):
distances, indices = index.search(np.array([query_embedding]), top_k)
results = [(stored_texts[i], distances[0][idx]) for idx, i in enumerate(indices[0])]
return results
# Function to create a PDF from the response
def create_pdf(response_text):
buffer = BytesIO()
c = canvas.Canvas(buffer, pagesize=letter)
width, height = letter
# Add the response text to the PDF
c.drawString(30, height - 30, "Intelligent Reply:")
text_object = c.beginText(30, height - 50)
text_object.setFont("Helvetica", 10)
text_object.setTextOrigin(30, height - 50)
# Add the response text, line by line
lines = response_text.split("\n")
for line in lines:
text_object.textLine(line)
c.drawText(text_object)
c.showPage()
c.save()
buffer.seek(0)
return buffer
# Streamlit app starts here
st.title("Course Query Assistant")
# Input OpenAI API key
openai_api_key = st.text_input("Enter your OpenAI API key:", type="password")
if openai_api_key:
openai.api_key = openai_api_key
# Upload course materials
uploaded_files = st.file_uploader("Upload Course Materials (PDFs)", type=["pdf"], accept_multiple_files=True)
if uploaded_files:
st.write("Processing uploaded course materials...")
# Extract text and generate embeddings for all uploaded PDFs
course_texts = []
for uploaded_file in uploaded_files:
text = extract_text_from_pdf(uploaded_file)
course_texts.append(text)
# Combine all course materials into one large text
combined_text = " ".join(course_texts)
# Split combined text into smaller chunks for embedding (max tokens ~1000)
chunks = [combined_text[i:i+1000] for i in range(0, len(combined_text), 1000)]
# Generate embeddings for all chunks
embeddings = [get_embeddings(chunk) for chunk in chunks]
# Convert the list of embeddings into a NumPy array (shape: [num_chunks, embedding_size])
embeddings_np = np.array(embeddings).astype("float32")
# Create a FAISS index for similarity search
index = faiss.IndexFlatL2(len(embeddings_np[0])) # Use the length of the embedding vectors for the dimension
index.add(embeddings_np)
st.write("Course materials have been processed and indexed.")
# User query
query = st.text_input("Enter your question about the course materials:")
if query:
# Generate embedding for the query
query_embedding = get_embeddings(query)
# Search for similar chunks in the FAISS index
results = search_similar(query_embedding, index, chunks)
# Create the context for the GPT prompt
context = "\n".join([result[0] for result in results])
modified_prompt = f"Context: {context}\n\nQuestion: {query}\n\nProvide a detailed answer based on the context."
# Get the GPT-3.5-turbo response
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": modified_prompt}]
)
# Extract the response text
response_text = response['choices'][0]['message']['content']
# Display the response
st.write("### Intelligent Reply:")
st.write(response_text)
# Button to download the response as PDF
pdf_buffer = create_pdf(response_text)
st.download_button(
label="Download Intelligent Reply as PDF",
data=pdf_buffer,
file_name="intelligent_reply.pdf",
mime="application/pdf"
)
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