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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
from streamlit_pdf_viewer import pdf_viewer
import hashlib
import io
import os
import pickle
import tempfile
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient.discovery import build
from googleapiclient.http import MediaFileUpload
import getpass
# Initialize API keys
google_api_key = 'AIzaSyDiZjRdBVZNqmhCQHnqDjz_fjgdfARyZp4'
tvly_api_key = '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'
openai_api_key = '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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.")
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="๐จโ๐ซ")
st.title("๐จโ๐ซ AI Professor")
# Authentication function for Google Drive
SCOPES = ['https://www.googleapis.com/auth/drive.file']
def authenticate_google_drive():
creds = None
if os.path.exists('token.pickle'):
with open('token.pickle', 'rb') as token:
creds = pickle.load(token)
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
'credentials.json', SCOPES)
creds = flow.run_local_server(port=0)
with open('token.pickle', 'wb') as token:
pickle.dump(creds, token)
return build('drive', 'v3', credentials=creds)
def upload_to_drive(content, filename="conversation.txt"):
service = authenticate_google_drive()
file_metadata = {'name': filename}
media = MediaFileUpload(filename, mimetype='text/plain')
with open(filename, 'w') as f:
f.write(content)
file = service.files().create(body=file_metadata, media_body=media, fields='id').execute()
st.success(f"Conversation uploaded to Google Drive! File ID: {file.get('id')}")
return file.get('id')
# Simple login system
def login():
username = st.text_input("Username", "")
password = st.text_input("Password", "", type="password")
if st.button("Login"):
if username == "admin" and password == "password123":
st.session_state.logged_in = True
st.success("Login successful!")
else:
st.session_state.logged_in = False
st.error("Invalid credentials. Please try again.")
# Initialize session state variables
if "logged_in" not in st.session_state:
st.session_state.logged_in = False
if not st.session_state.logged_in:
login()
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="gpt-4o-mini",
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()
# If logged in, continue with the chatbot functionality
if st.session_state.logged_in:
# Initialize session state variables
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
# Sidebar for PDF upload and settings
with st.sidebar:
st.title("Menu:")
pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=False)
quiz_button = st.button("๐๏ธ Make a quiz")
video_button = st.button("๐บ Search a video")
view = st.toggle("๐๏ธ 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)
# 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)
# Process PDF upload
if pdf_docs:
# Convert PDF to text and split into chunks for embedding
text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(text)
st.session_state.vector_store = get_vector_store(text_chunks)
st.success("Document uploaded and ready for conversation.")
# Process user query
user_query = st.chat_input("Type your message here...")
if user_query:
st.session_state.chat_history.append(HumanMessage(content=user_query))
with st.chat_message("Human"):
st.write(user_query)
response = get_response(user_query, st.session_state.chat_history, st.session_state.vector_store)
st.session_state.chat_history.append(AIMessage(content=response))
with st.chat_message("AI"):
st.write(response)
# Upload conversation to Google Drive
# upload_to_drive("".join([msg.content for msg in st.session_state.chat_history]), "chat_conversation.txt")
# 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.
Format each question like this:
Question X:
**A)** Answer 1
**B)** Answer 2
**C)** Answer 3
**D)** Answer 4
"""
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 and the last conversation.
"""
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"๐บ Here's a video about {response}: {youtube_url}")
st.video(youtube_url)
video_message = f"๐บ Here's a video about {response}:\n{youtube_url}"
st.session_state.chat_history.append(AIMessage(content=video_message)) |