Create app.py

app.py

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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="👨‍🏫")
+    st.title("👨‍🏫 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("🗒️ Make a quiz", type="primary")
+        video_button = st.button("📺 Search a video on the topic")
+        view = st.toggle("👁️ 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"📺 Here's a video about {response}:")
+                    st.video(youtube_url)
+                    video_message = (
+                        f"📺 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