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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 io import StringIO
# 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 generate code based on a prompt
def generate_code_from_prompt(prompt, model="gpt-4o-mini"):
response = openai.ChatCompletion.create(
model=model,
messages=[{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
# Function to save code to a .txt file
def save_code_to_file(code, filename="generated_code.txt"):
with open(filename, "w") as f:
f.write(code)
# Function to generate AI-based study notes and summaries
def generate_summary(text):
prompt = f"Summarize the following text into key points:\n\n{text}"
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}]
)
return response['choices'][0]['message']['content']
# Streamlit app starts here
st.title("AI Assistance")
# 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
# Sidebar to toggle between Course Query Assistant and Code Generator
st.sidebar.title("Select Mode")
mode = st.sidebar.radio("Choose an option", ("Course Query Assistant", "Code Generator", "AI Chatbot Tutor", "AI Study Notes & Summaries"))
if mode == "Course Query Assistant":
st.header("Course Query Assistant")
# 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)
elif mode == "Code Generator":
st.header("Code Generator")
# Code generation prompt input
code_prompt = st.text_area("Describe the code you want to generate:",
"e.g., Write a Python program that generates Fibonacci numbers.")
if st.button("Generate Code"):
if code_prompt:
with st.spinner("Generating code..."):
# Generate code using GPT-4
generated_code = generate_code_from_prompt(code_prompt)
# Clean the generated code to ensure only code is saved (removing comments or additional text)
clean_code = "\n".join([line for line in generated_code.splitlines() if not line.strip().startswith("#")])
# Save the clean code to a file
save_code_to_file(clean_code)
# Display the generated code
st.write("### Generated Code:")
st.code(clean_code, language="python")
# Provide a download link for the generated code
with open("generated_code.txt", "w") as f:
f.write(clean_code)
st.download_button(
label="Download Generated Code",
data=open("generated_code.txt", "rb").read(),
file_name="generated_code.txt",
mime="text/plain"
)
else:
st.error("Please provide a prompt to generate the code.")
elif mode == "AI Chatbot Tutor":
st.header("AI Chatbot Tutor")
# Chat interface for the AI tutor
chat_history = []
def chat_with_bot(query):
chat_history.append({"role": "user", "content": query})
response = openai.ChatCompletion.create(
model="gpt-4o-mini",
messages=chat_history
)
chat_history.append({"role": "assistant", "content": response['choices'][0]['message']['content']})
return response['choices'][0]['message']['content']
user_query = st.text_input("Ask a question:")
if user_query:
with st.spinner("Getting answer..."):
bot_response = chat_with_bot(user_query)
st.write(f"### AI Response: {bot_response}")
elif mode == "AI Study Notes & Summaries":
st.header("AI Study Notes & Summaries")
# Upload course materials for summarization
uploaded_files_for_summary = st.file_uploader("Upload Course Materials (PDFs) for Summarization", type=["pdf"], accept_multiple_files=True)
if uploaded_files_for_summary:
st.write("Generating study notes and summaries...")
# Extract text from PDFs
all_text = ""
for uploaded_file in uploaded_files_for_summary:
text = extract_text_from_pdf(uploaded_file)
all_text += text
# Generate summary using AI
summary = generate_summary(all_text)
# Display the summary
st.write("### AI-Generated Summary:")
st.write(summary) |