app.py

import os
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI
from langchain.memory import ConversationSummaryMemory
import gradio as gr
from PyPDF2 import PdfReader
from langchain.agents import initialize_agent, Tool
from langchain_core.exceptions import OutputParserException

apiKey = os.getenv("OPENAI_API_KEY")


# Load PDF
def read_pdf(file_paths):
    combined_text = ""
    for file_path in file_paths:
        with open(file_path, "rb") as file:
            reader = PdfReader(file)
            text = ""
            for page in reader.pages:
                text += page.extract_text()
            combined_text += text + "\n\n"  
    return combined_text

pdf_file_path = ["property_law.pdf","ipc.pdf","constitution_of_india.pdf","ipc_2.pdf","cn_2.pdf","pl_2.pdf"]
document_text = read_pdf(pdf_file_path)

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,  
    chunk_overlap=100 
)

chunks = text_splitter.split_text(document_text)

embeddings = OpenAIEmbeddings(openai_api_key=apiKey)
vector_db = FAISS.from_texts(chunks, embeddings)

exceptionMsg = "Sorry, I couldn't understand your question. Please ask a specific question regarding IPC, Transfer of Property and Constitution of India."


def retrieve_from_db(query):
    results = vector_db.similarity_search(query, k=1)
    return results[0].page_content


llm = ChatOpenAI(openai_api_key=apiKey)


tools = [
    Tool(
        name="Legal-Library",
        func=retrieve_from_db,
        description=(
        "Searches a legal document database including the Indian Penal Code, "
        "Constitution of India, and Transfer of Property Act to retrieve accurate, "
        "contextual, and relevant legal information. Use this tool for queries "
        "related to specific laws, sections, or provisions in these documents."
        )
    )
]


memory = ConversationSummaryMemory(llm=llm)
agent = initialize_agent(
    tools=tools,
    agent_type="zero-shot-react-description",
    llm=llm,
    memory=memory,
    handle_parsing_errors=True
)


def chatbot(input_text, chat_history):
    try:    
        response = agent.run(input_text)

        if response == "N/A":
            response = exceptionMsg
        
        memory.save_context({"user": input_text}, {"assistant": response})

        chat_history.append([input_text, response])
        
        return chat_history, ""
    
    except OutputParserException as e:
        error_message = exceptionMsg
        chat_history.append([error_message, input_text])
        print("Error:", str(e))

        return chat_history, ""
    
def clear_chat():
    return [],""

def gradio_interface():
    with gr.Blocks() as demo:
        gr.Markdown("""
            <div style="text-align: center;">
                <h1>Legal Query Chatbot</h1>
            </div>
        """)

        with gr.Column():
            chatbot_ui = gr.Chatbot()
            user_input = gr.Textbox(placeholder="Ask your legal questions here, such as IPC sections, property laws, constitution articles.")
            submit_button = gr.Button("Submit", elem_classes="gr-button", variant="primary")
            clear_chat_button = gr.Button("Clear Chat", variant="secondary")
            submit_button.click(fn=chatbot, inputs=[user_input, chatbot_ui], outputs=[chatbot_ui, user_input])
            user_input.submit(fn=chatbot, inputs=[user_input, chatbot_ui], outputs=[chatbot_ui, user_input])
            clear_chat_button.click(fn=clear_chat, inputs=None, outputs=[chatbot_ui, user_input])


    return demo


app = gradio_interface()

if __name__ == "__main__":
    app.launch()