Upload 2 files

app.py

@@ -0,0 +1,271 @@
+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="👨‍🏫")
+st.title("👨‍🏫 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("🗒️ Make a quiz")
+        video_button = st.button("📺 Search a video")
+        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)
+
+    # 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"📺 Here's a video about {response}: {youtube_url}")
+                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))

requirements.txt

@@ -0,0 +1,13 @@
+streamlit
+langchain_core
+langchain_openai
+langchain_google_genai
+langchain_community
+PyPDF2
+faiss-cpu
+tavily-python
+streamlit-pdf-viewer
+google-auth
+google-auth-oauthlib
+google-auth-httplib2
+google-api-python-client