app.py

import streamlit as st
import os
import pickle
import time
import g4f
import tempfile
import PyPDF2
from pdf2image import convert_from_path
import pytesseract

st.set_page_config(page_title="EDUCATIONAL ASSISTANT")

st.markdown(
    """
    <style>
        .title {
            text-align: center;
            font-size: 2em;
            font-weight: bold;
        }
    </style>
    <div class="title"> 📚 EDUCATIONAL ASSISTANT 📚</div>
    """,
    unsafe_allow_html=True
)
# Load and Save Conversations
conversations_file = "conversations.pkl"


@st.cache_data
def load_conversations():
    try:
        with open(conversations_file, "rb") as f:
            return pickle.load(f)
    except (FileNotFoundError, EOFError):
        return []


def save_conversations(conversations):
    temp_conversations_file = conversations_file
    with open(temp_conversations_file, "wb") as f:
        pickle.dump(conversations, f)
    os.replace(temp_conversations_file, conversations_file)


if 'conversations' not in st.session_state:
    st.session_state.conversations = load_conversations()

if 'current_conversation' not in st.session_state:
    st.session_state.current_conversation = [{"role": "assistant", "content": "How may I assist you today?"}]


def truncate_string(s, length=30):
    return s[:length].rstrip() + "..." if len(s) > length else s


def display_chats_sidebar():
    with st.sidebar.container():
        st.header('Settings')
        col1, col2 = st.columns([1, 1])

        with col1:
            if col1.button('Start New Chat', key="new_chat"):
                st.session_state.current_conversation = []
                st.session_state.conversations.append(st.session_state.current_conversation)

        with col2:
            if col2.button('Clear All Chats', key="clear_all"):
                st.session_state.conversations = []
                st.session_state.current_conversation = []

    if st.sidebar.button('Solve Assignment', key="summarize_bills", use_container_width=True):
        st.session_state.page = "summarize_bills"

    with st.sidebar.container():
        st.header('Conversations')
        for idx, conversation in enumerate(st.session_state.conversations):
            if conversation:
                chat_title_raw = next((msg["content"] for msg in conversation if msg["role"] == "user"), "New Chat")
                chat_title = truncate_string(chat_title_raw)
                if st.sidebar.button(f"{chat_title}", key=f"chat_button_{idx}"):
                    st.session_state.current_conversation = st.session_state.conversations[idx]


def summarize_bill():
    st.header("📚 Solve PDF Assignments 📜")

    if st.button("Back to Chat"):
        st.session_state.page = "chat"

    uploaded_file = st.file_uploader("Upload an Agreement", type=['pdf'])
    if uploaded_file is not None:
        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
            tmp_file.write(uploaded_file.read())
            extracted_text = extract_text_from_pdf(tmp_file.name)

        if st.button('Solve'):
            # Assuming g4f.ChatCompletion can be used for summarization
            # Replace with appropriate summarization logic if needed
            summary = g4f.ChatCompletion.create(
                model="gpt-3.5-turbo",
                messages=[{"role": "user", "content": "Please solve this Agreement: \n" + extracted_text}],
                temperature=0.5,  # You can adjust parameters as needed
                max_tokens=150  # Adjust the token limit as needed
            )
            st.text_area("Summary", summary, height=400)


def extract_text_from_pdf(file_path: str) -> str:
    try:
        with open(file_path, 'rb') as file:
            reader = PyPDF2.PdfReader(file)
            text = ''
            for page_number in range(len(reader.pages)):
                page = reader.pages[page_number]
                text += page.extract_text()
        return text
    except Exception as e:
        try:
            images = convert_from_path(file_path)
            extracted_texts = [pytesseract.image_to_string(image) for image in images]
            return "\n".join(extracted_texts)
        except Exception as e:
            raise ValueError(f"Failed to process {file_path} using PDF Reader and OCR. Error: {e}")


def main_app():
    for message in st.session_state.current_conversation:
        with st.chat_message(message["role"]):
            st.write(message["content"])

    def generate_response(prompt_input):
        string_dialogue = '''
         You are an educational assistant chatbot, designed to provide insightful and accurate answers in the educational domain. Your responses should be engaging and emulate a human educator to create a comfortable learning environment. Instead of simply presenting facts, aim to inspire curiosity and deeper understanding.
        
        Context:
        
            Understand the essence of the user's educational query.
            Consider the academic level and subject matter of the question.
            Access a broad knowledge base to provide well-informed responses.
            Organize the response clearly and logically.
            Deliver the answer in a manner that is both educational and relatable to human interaction.
        
        Human:
        '''
        for dict_message in st.session_state.current_conversation:
            string_dialogue += dict_message["role"].capitalize() + ": " + dict_message["content"] + "\\n\\n"

        prompt = f"{string_dialogue}\n  {prompt_input} Assistant: "
        response_generator = g4f.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "user", "content": prompt}],
            stream=True,
        )
        return response_generator

    if prompt := st.chat_input('Send a Message'):
        st.session_state.current_conversation.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.write(prompt)

        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                response = generate_response(prompt)
                placeholder = st.empty()
                full_response = ''
                for item in response:
                    full_response += item
                    time.sleep(0.003)
                    placeholder.markdown(full_response)
                placeholder.markdown(full_response)
                st.session_state.current_conversation.append({"role": "assistant", "content": full_response})
                save_conversations(st.session_state.conversations)


display_chats_sidebar()
if st.session_state.get('page') == "summarize_bills":
    summarize_bill()
elif st.session_state.get('page') == "chat":
    main_app()
else:
    # Default page when the app starts or when the state is not set
    main_app()