app.py

import os
import requests
from io import BytesIO
from PyPDF2 import PdfReader
from tempfile import NamedTemporaryFile
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from groq import Groq
import gradio as gr

# Initialize Groq client
client = Groq(api_key="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")

# Predefined list of Google Drive links
drive_links = [
    "https://drive.google.com/file/d/1x83IIMfuFPFuCzZiRJfT0obBf9PUWHA2/view",
    # Add more links here as needed
]

# Function to download PDF from Google Drive
def download_pdf_from_drive(drive_link):
    file_id = drive_link.split('/d/')[1].split('/')[0]
    download_url = f"https://drive.google.com/uc?id={file_id}&export=download"
    response = requests.get(download_url)
    if response.status_code == 200:
        return BytesIO(response.content)
    else:
        raise Exception("Failed to download the PDF file from Google Drive.")

# Function to extract text from a PDF
def extract_text_from_pdf(pdf_stream):
    pdf_reader = PdfReader(pdf_stream)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

# Function to split text into chunks
def chunk_text(text, chunk_size=500, chunk_overlap=50):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size, chunk_overlap=chunk_overlap
    )
    return text_splitter.split_text(text)

# Function to create embeddings and store them in FAISS
def create_embeddings_and_store(chunks):
    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    vector_db = FAISS.from_texts(chunks, embedding=embeddings)
    return vector_db

# Function to query the vector database and interact with Groq
def query_vector_db(query, vector_db):
    # Retrieve relevant documents
    docs = vector_db.similarity_search(query, k=3)
    context = "\n".join([doc.page_content for doc in docs])

    # Interact with Groq API
    chat_completion = client.chat.completions.create(
        messages=[
            {"role": "system", "content": f"Use the following context:\n{context}"},
            {"role": "user", "content": query},
        ],
        model="llama3-8b-8192",
    )
    return chat_completion.choices[0].message.content

# Process the predefined Google Drive links
def process_drive_links():
    all_chunks = []
    for link in drive_links:
        try:
            # Download PDF
            pdf_stream = download_pdf_from_drive(link)
            # Extract text
            text = extract_text_from_pdf(pdf_stream)
            # Chunk text
            chunks = chunk_text(text)
            all_chunks.extend(chunks)
        except Exception as e:
            return f"Error processing link {link}: {e}"
    
    if all_chunks:
        # Generate embeddings and store in FAISS
        vector_db = create_embeddings_and_store(all_chunks)
        return vector_db
    return None

# Gradio interface
vector_db = process_drive_links()

def gradio_query_interface(user_query):
    if vector_db is None:
        return "Error: Could not process Google Drive links."
    if not user_query:
        return "Please enter a query."
    response = query_vector_db(user_query, vector_db)
    return response

iface = gr.Interface(
    fn=gradio_query_interface,
    inputs=gr.Textbox(label="Enter your query:"),
    outputs=gr.Textbox(label="Response from LLM:"),
    title="BISE Buddy - A RAG-Based Application with Google Drive Support",
    description="This application processes predefined Google Drive links, extracts text, and uses embeddings for querying."
)

iface.launch()