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import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_community.vectorstores import FAISS
from tavily import TavilyClient
import hashlib
from streamlit_pdf_viewer import pdf_viewer
import tempfile
import os
USER_CREDENTIALS = {"admin": "admin"} # Replace this with your username: password pairs
# Function to check if the user is authenticated
def check_login(username, password):
if USER_CREDENTIALS.get(username) == password:
return True
return False
# Function to handle login page
def login_page():
st.title("Login Page")
username = st.text_input("Username")
password = st.text_input("Password", type="password")
if st.button("Login"):
if check_login(username, password):
st.session_state.authenticated = True
st.session_state.username = username
st.session_state.password = password # You can store password or omit it
st.success(f"Welcome, {username}!")
st.rerun() # Rerun the app to show the main page after login
else:
st.error("Invalid credentials")
def main_app():
# Initialize API keys
# Option 1: Using environment variables
google_api_key = "AIzaSyDiZjRdBVZNqmhCQHnqDjz_fjgdfARyZp4"
tvly_api_key = "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"
openai_api_key = "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"
# Option 2: Using Streamlit secrets (uncomment if using secrets.toml)
# if 'google_api_key' in st.secrets:
# google_api_key = st.secrets['AIzaSyDiZjRdBVZNqmhCQHnqDjz_fjgdfARyZp4']
# tvly_api_key = st.secrets['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']
# openai_api_key = st.secrets['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']
# Validate API keys
if not all([google_api_key, tvly_api_key, openai_api_key]):
st.error("Please set up your API keys in environment variables or secrets.toml")
st.stop()
# Initialize Tavily client
web_tool_search = TavilyClient(api_key=tvly_api_key)
# Set up Streamlit page
st.set_page_config(page_title="AI Professor", page_icon="π¨βπ«")
st.title("π¨βπ« AI Professor")
def get_pdf_text(pdf_docs):
text = ""
if isinstance(pdf_docs, list):
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
else:
pdf_reader = PdfReader(pdf_docs)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=10000, chunk_overlap=1000
)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
try:
embeddings = GoogleGenerativeAIEmbeddings(
model="models/embedding-001", google_api_key=google_api_key
)
vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
return vector_store
except Exception as e:
st.error(f"Error creating vector store: {str(e)}")
return None
def get_response(user_query, chat_history, vector_store):
if vector_store is None:
return "Please upload a PDF document first."
template = """
You are a helpful assistant. Answer the following questions considering the history of the conversation and the document provided:
Context: {context}
Chat history: {chat_history}
User question: {user_question}
"""
prompt = ChatPromptTemplate.from_template(template)
try:
llm = ChatOpenAI(
base_url="https://api.groq.com/openai/v1",
api_key=openai_api_key,
model_name="llama-3.1-8b-instant",
temperature=1,
max_tokens=1024,
)
docs = vector_store.similarity_search(user_query)
context = "\n".join(doc.page_content for doc in docs)
chain = prompt | llm | StrOutputParser()
return chain.invoke(
{
"context": context,
"chat_history": chat_history,
"user_question": user_query,
}
)
except Exception as e:
return f"Error generating response: {str(e)}"
def get_youtube_url(query):
try:
response = web_tool_search.search(
query=query,
search_depth="basic",
include_domains=["youtube.com"],
max_results=1,
)
for result in response["results"]:
if "youtube.com/watch" in result["url"]:
return result["url"]
return None
except Exception as e:
st.error(f"Error searching for video: {str(e)}")
return None
def get_pdfs_hash(pdf_docs):
combined_hash = hashlib.md5()
if isinstance(pdf_docs, list):
for pdf in pdf_docs:
content = pdf.read()
combined_hash.update(content)
pdf.seek(0)
else:
content = pdf_docs.read()
combined_hash.update(content)
pdf_docs.seek(0)
return combined_hash.hexdigest()
# Initialize session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = [
AIMessage(
content="Hello, I am Chatbot professor assistant. How can I help you?"
),
]
if "vector_store" not in st.session_state:
st.session_state.vector_store = None
if "current_pdfs_hash" not in st.session_state:
st.session_state.current_pdfs_hash = None
# Display chat history
for message in st.session_state.chat_history:
if isinstance(message, AIMessage):
with st.chat_message("AI"):
st.write(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message("Human"):
st.write(message.content)
# Chat input
user_query = st.chat_input("Type your message here...")
# Sidebar
with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader(
"Upload your PDF Files", accept_multiple_files=False, key="pdf_uploader"
)
quiz_button = st.button("ποΈ Make a quiz", type="primary")
video_button = st.button("πΊ Search a video on the topic")
view = st.toggle("ποΈ View PDF")
if view and pdf_docs:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(pdf_docs.read())
temp_pdf_path = temp_file.name
pdf_viewer(temp_pdf_path, width=800)
# Custom CSS for sidebar
st.markdown(
"""
<style>
section[data-testid="stSidebar"] {
width: 600px;
min-width: 600px;
max-width: 800px;
background-color: #f0f2f6;
}
.css-1lcbmhc {
margin-left: 360px;
padding: 1rem;
}
.block-container {
max-width: 800px;
min-width: 600px;
margin: auto;
}
.stChatMessage {
width: 100%;
max-width: 800px;
margin: 0 auto;
}
</style>
""",
unsafe_allow_html=True,
)
# Process PDF upload
if pdf_docs:
new_hash = get_pdfs_hash(pdf_docs)
if new_hash != st.session_state.current_pdfs_hash:
text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(text)
st.session_state.vector_store = get_vector_store(text_chunks)
st.session_state.current_pdfs_hash = new_hash
st.success("The document has been updated!")
# Handle user query
if user_query:
st.session_state.chat_history.append(HumanMessage(content=user_query))
with st.chat_message("Human"):
st.markdown(user_query, unsafe_allow_html=True)
with st.chat_message("AI"):
with st.spinner("Thinking..."):
response = get_response(
user_query,
st.session_state.chat_history,
st.session_state.vector_store,
)
st.write(response)
st.session_state.chat_history.append(AIMessage(content=response))
# Show message if no PDF is uploaded
if pdf_docs is None:
st.write("Please upload your PDF course before starting the chat.")
# Handle quiz generation
if quiz_button:
with st.spinner("Generating quiz..."):
quiz_prompt = """
Based on the document content, create a quiz with 5 multiple choice questions.
For each question:
1. Ask a clear, specific question
2. Provide 4 options labeled A, B, C, D
3. Make sure the options are plausible but distinct
4. Don't reveal the correct answer
Format each question like this:
Question X:
**A)**
**B)**
**C)**
**D)**
"""
with st.chat_message("AI"):
response = get_response(
quiz_prompt,
st.session_state.chat_history,
st.session_state.vector_store,
)
st.write(response)
st.session_state.chat_history.append(AIMessage(content=response))
# Handle video search
if video_button:
with st.spinner("Searching for relevant video..."):
video_prompt = """
Extract the main topic and key concepts from the document or from the last conversation in 3-4 words maximum.
Focus on the core subject matter only.
Do not include any additional text or explanation.
Example format: "machine learning neural networks" or "quantum computing basics"
"""
with st.chat_message("AI"):
response = get_response(
video_prompt,
st.session_state.chat_history,
st.session_state.vector_store,
)
youtube_url = get_youtube_url(f"Course on {response}")
if youtube_url:
st.write(f"πΊ Here's a video about {response}:")
st.video(youtube_url)
video_message = (
f"πΊ Here's a video about {response}:\n{youtube_url}"
)
st.session_state.chat_history.append(
AIMessage(content=video_message)
)
# Check if the user is authenticated
if "authenticated" not in st.session_state or not st.session_state.authenticated:
login_page() # Show login page if not authenticated
else:
main_app() # Show the main app if authenticated
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