app.py

import time
import os
import streamlit as st
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from googleapiclient.discovery import build
from google.oauth2.service_account import Credentials
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain.chains import ConversationalRetrievalChain
from langchain_together import Together

from footer import footer
 
# Google Drive API setup
SCOPES = ["https://www.googleapis.com/auth/drive.readonly"]
SERVICE_ACCOUNT_FILE = "data/credentials.json"  # Path to your Google API credentials file
FOLDER_ID = "1LZIx-1tt_GormpU8nF_I2WL88Oxa9juU"  # Replace with your Google Drive folder ID

def authenticate_drive():
    """Authenticate and return the Google Drive API service."""
    creds = Credentials.from_service_account_file(SERVICE_ACCOUNT_FILE, scopes=SCOPES)
    return build("drive", "v3", credentials=creds)



from fuzzywuzzy import process

def search_drive_file(file_name):
    """Search for a file by name in the specified Google Drive folder using fuzzy matching."""
    service = authenticate_drive()
    try:
        # Get all files in the folder
        query = f"'{FOLDER_ID}' in parents and trashed=false"
        results = service.files().list(q=query, fields="files(id, name)").execute()
        files = results.get("files", [])
        
        # Debug: Print all file names for inspection
        st.write("Available files:", [f['name'] for f in files])
        
        # Perform fuzzy matching to find the best match
        file_names = [f['name'] for f in files]
        best_match, score = process.extractOne(file_name, file_names)

        if score >= 75:  # Threshold for a match
            matched_file = next(f for f in files if f['name'] == best_match)
            st.write(f"Match found: {matched_file['name']} (Score: {score})")
            return [matched_file]
        else:
            st.warning(f"No close matches found for '{file_name}'. Try rephrasing or checking the folder manually.")
            return []

    except Exception as e:
        st.error(f"An error occurred: {e}")
        return []

# Set the Streamlit page configuration and theme
st.set_page_config(page_title="In-Legal-IPC", layout="centered")

# Display the logo image with blur shadow
col1, col2, col3 = st.columns([1, 30, 1])
with col2:
    st.markdown(
        """
        <style>
        .blur-shadow {
            box-shadow: 0px 4px 20px rgba(0, 0, 0, 0.5);
            border-radius: 10px;
        }
        </style>
        <div class="blur-shadow">
            <img src="https://raw.githubusercontent.com/shiv4321/Images/refs/heads/main/Banner.png" alt="Banner" width="100%">
        </div>
        """,
        unsafe_allow_html=True
    )

def hide_hamburger_menu():
    st.markdown("""
        <style>
            #MainMenu {visibility: hidden;}
            footer {visibility: hidden;}
        </style>
        """, unsafe_allow_html=True)

hide_hamburger_menu()

# Initialize session state for messages and memory
if "messages" not in st.session_state:
    st.session_state.messages = []

if "memory" not in st.session_state:
    st.session_state.memory = ConversationBufferWindowMemory(k=5, memory_key="chat_history", return_messages=True)

@st.cache_resource
def load_embeddings():
    """Load and cache the embeddings model."""
    return HuggingFaceEmbeddings(model_name="law-ai/InLegalBERT")

embeddings = load_embeddings()
db = FAISS.load_local("ipc_embed_db", embeddings, allow_dangerous_deserialization=True)
db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 3})

# Define the prompt template
prompt_template = """
<s>[INST]
As a legal chatbot specializing in the Indian Penal Code, you are tasked with providing highly accurate and contextually appropriate responses. Ensure your answers meet these criteria:
- Respond in a bullet-point format to clearly delineate distinct aspects of the legal query.
- Each point should accurately reflect the breadth of the legal provision in question, avoiding over-specificity unless directly relevant to the user's query.
- Clarify the general applicability of the legal rules or sections mentioned, highlighting any common misconceptions or frequently misunderstood aspects.
- Limit responses to essential information that directly addresses the user's question, providing concise yet comprehensive explanations.
- Avoid assuming specific contexts or details not provided in the query, focusing on delivering universally applicable legal interpretations unless otherwise specified.
- Conclude with a brief summary that captures the essence of the legal discussion and corrects any common misinterpretations related to the topic.
CONTEXT: {context}
CHAT HISTORY: {chat_history}
QUESTION: {question}
ANSWER:
- [Detail the first key aspect of the law, ensuring it reflects general application]
- [Provide a concise explanation of how the law is typically interpreted or applied]
- [Correct a common misconception or clarify a frequently misunderstood aspect]
- [Detail any exceptions to the general rule, if applicable]
- [Include any additional relevant information that directly relates to the user's query]
</s>[INST]
"""

prompt = PromptTemplate(template=prompt_template,
                        input_variables=['context', 'question', 'chat_history'])

api_key = os.getenv('TOGETHER_API_KEY')
llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.5, max_tokens=1024, together_api_key="7c9bbd129ef15842ca5205190e3f93cea81dd1a6b19c33e1ea5da635b6db1bb2")

qa = ConversationalRetrievalChain.from_llm(llm=llm, memory=st.session_state.memory, retriever=db_retriever, combine_docs_chain_kwargs={'prompt': prompt})

def extract_answer(full_response):
    """Extracts the answer from the LLM's full response by removing the instructional text."""
    answer_start = full_response.find("Response:")
    if answer_start != -1:
        answer_start += len("Response:")
        answer_end = len(full_response)
        return full_response[answer_start:answer_end].strip()
    return full_response

def reset_conversation():
    st.session_state.messages = []
    st.session_state.memory.clear()

# Function to create a PDF
def create_pdf(content):
    pdf_filename = "legal_letter.pdf"
    c = canvas.Canvas(pdf_filename, pagesize=letter)
    width, height = letter
    c.drawString(100, height - 100, content)
    c.save()
    return pdf_filename

# Add links to multiple PDFs just above the chat input
st.markdown("<h3 class='underline'>Useful PDFs</h3>", unsafe_allow_html=True)

col1, col2 = st.columns(2)  # Create two columns for better alignment
with col1:
    if st.button("Commercial Court Rules and Forms 📄", key="ccrf", help="Open PDF", use_container_width=True):
        st.markdown("[Open PDF](https://drive.google.com/file/d/198SC1mKipJ7WQXGN-5uc8qkNV5rLxVlT/view?usp=sharing)", unsafe_allow_html=True)
    if st.button("Bail-Bond 📄", key="bb", help="Open PDF", use_container_width=True):
        st.markdown("[Open PDF](https://drive.google.com/file/d/1Eju14MgFFME3nUknjwlbU8C9nrQoeM1v/view?usp=drive_link)", unsafe_allow_html=True)

with col2:
    if st.button("Inspection Form 📄", key="if", help="Open PDF", use_container_width=True):
        st.markdown("[Open PDF](https://drive.google.com/file/d/17FT5Pmgp4bgf31tFyQRMNVnoRuVlQ2zi/view?usp=sharing)", unsafe_allow_html=True)
    if st.button("Additional PDF 📄", key="apdf", help="Open PDF", use_container_width=True):
        st.markdown("[Open PDF](https://drive.google.com/file/d/1LY1-R9chmd_I7Tf3iC4jNZ5dHRFFkjaV/view?usp=sharing)", unsafe_allow_html=True)

# Add CSS for the button styling
st.markdown("""
    <style>
    .stButton button {
        background-color: #ADD8E6;
        color: black;
        font-size: 16px;
    }
    </style>
    """, unsafe_allow_html=True)

# Display previous messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

# Initialize session state variables
if "show_reset" not in st.session_state:
    st.session_state.show_reset = False

# Chat input area
input_prompt = st.chat_input("Say something...")
if input_prompt:
    with st.chat_message("user"):
        st.markdown(f"**You:** {input_prompt}")

    # Enable the reset button after receiving input
    st.session_state.show_reset = True

    if "form" in input_prompt.lower() or "document" in input_prompt.lower():
        with st.spinner("Searching Google Drive..."):
            # Call the updated search function
            search_results = search_drive_file(input_prompt)
        
        if search_results:
            # Generate response for found files
            response = "🔍 Document(s) found! Click below to view:"
            for file in search_results:
                response += f"\n- [{file['name']}](https://drive.google.com/file/d/{file['id']}/view)"
            st.session_state.messages.append({"role": "assistant", "content": response})
            st.write(response)
        else:
            # If no results, provide an alternative message
            response = (
                "⚠️ No matching documents found. "
                "Please check the spelling or explore the folder directly: "
                f"[Google Drive Folder](https://drive.google.com/drive/folders/{FOLDER_ID})"
            )
            st.session_state.messages.append({"role": "assistant", "content": response})
            st.write(response)

    else:
        # Handle general questions
        with st.chat_message("assistant"):
            with st.spinner("Thinking 💡..."):
                try:
                    # Validate the input before invoking the QA chain
                    if not input_prompt.strip():
                        st.warning("⚠️ Input cannot be empty!")
                    else:
                        result = qa.invoke(input=input_prompt)
                        answer = result["answer"].strip()

                        # Simulate typing effect for the response
                        message_placeholder = st.empty()
                        full_response = (
                            "⚠️ **_Gentle reminder: We strive for precision, but please double-check._**\n\n"
                        )
                        for chunk in answer.split():
                            full_response += chunk + " "
                            time.sleep(0.02)  # Simulating typing
                            message_placeholder.markdown(full_response + " |", unsafe_allow_html=True)
                        
                        st.session_state.messages.append({"role": "assistant", "content": answer})

                except Exception as e:
                    # Handle unexpected errors during QA invocation
                    error_message = f"⚠️ **_Error: An unexpected issue occurred: {str(e)}._**"
                    st.error(error_message)
                    st.session_state.messages.append({"role": "assistant", "content": error_message})

# Reset button
if st.session_state.show_reset:
    if st.button('🗑️ Reset All Chat', on_click=reset_conversation):
        st.rerun()  # Updated from st.experimental_rerun

footer()