File size: 3,573 Bytes
818bad6 7a25c1c cc699e9 818bad6 7a25c1c 818bad6 7a25c1c 818bad6 7a25c1c 818bad6 4287e6f 818bad6 cc699e9 818bad6 291f44e 833bbde ca29ecb 818bad6 cc699e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
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
# 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
# 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-4 response
response = openai.ChatCompletion.create(
model="gpt-4o-mini", # Update to GPT-4 (or your desired model)
messages=[{"role": "user", "content": modified_prompt}]
)
# Get the response content
response_content = response['choices'][0]['message']['content']
# Display the response in Streamlit (Intelligent Reply)
st.write("### Intelligent Reply:")
st.write(response_content) |