import streamlit as st # Define appliance options appliance_options = ["Refrigerator", "Dish Washer", "Coffee Maker", "Air Conditioner", "Washing Machine", "Laptop", "Oven", "TV", "Soundbar", "Vacuum Cleaner", "Iron", "Mixer", "Food Processor", "Tooth Brush", "Electric Toaster", "Citrus Press", "Air Dryer", "Juicer", "Heater", "Ceiling Fan"] # Define brand dictionaries for each appliance (key: appliance name, value: list of brands) brand_options = { "Refrigerator": ["Haier", "Samsung", "Dawlance", "Hitachi", "PEL"], "Dish Washer": ["Samsung", "Bosch", "Baumeatic"], "Coffee Maker": ["Black & Decker"], "Air Conditioner": ["Samsung", "Haier", "GREE"], "Washing Machine": ["Samsung", "Haier"], "Laptop": ["Haier"], "Oven": ["La Vita", "Samsung", "Kenwood", "Haier", "Phillips"], "TV": ["Samsung", "Haier", "Hisense", "Phillips", "Google"], "Soundbar": ["Samsung", "Phillips"], "Vacuum Cleaner": ["Samsung"], "Iron": ["Phillips"], "Mixer": ["Phillips"], "Food Processor": ["Phillips"], "Tooth Brush": ["Phillips"], "Electric Toaster": ["Phillips"], "Citrus Press": ["Phillips"], "Air Dryer": ["Phillips"], "Juicer": ["Phillips"], "Heater": ["Phillips"], "Ceiling Fan": ["GFC", "Hunter"] } # Get user selections for appliance and brand selected_appliance = st.selectbox("Select Appliance", appliance_options) selected_brand = None if selected_appliance: selected_brand = st.selectbox(f"Select Brand for {selected_appliance}", brand_options[selected_appliance]) # Display user selection if selected_appliance and selected_brand: st.write(f"You selected {selected_brand} {selected_appliance}") import os import requests from io import BytesIO from groq import Groq from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import FAISS from langchain.text_splitter import RecursiveCharacterTextSplitter from PyPDF2 import PdfReader from tempfile import NamedTemporaryFile # Initialize Groq client client = Groq(api_key="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") #client = Groq("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") # Predefined list of Google Drive links drive_links = [ "https://drive.google.com/file/d/15ep4h17VFarb4coWAgqcEUDUTN7Ek6dr/view?usp=sharing", "https://drive.google.com/file/d/1ocxUsw4rVc9MNV_9wARvXg9D9SLe2vVi/view?usp=sharing", "https://drive.google.com/file/d/1yrOTIlOYywjbfeMWcBG1kBK8-LLYGl3t/view?usp=sharing", "https://drive.google.com/file/d/1otdel-JxaZBvitQQMiXtniTNAsmICrMT/view?usp=sharing", "https://drive.google.com/file/d/16--HjKNn5Oz1cqX3b-8qvwnD0_yLvdy2/view?usp=sharing", "https://drive.google.com/file/d/1lnBD-0H1mJJ8N-lNdrhomuzgm8nRFVxA/view?usp=sharing", "https://drive.google.com/file/d/1ni7IxoI7j2wzCcWjQEEqrE1JO0HsRGrl/view?usp=sharing", "https://drive.google.com/file/d/1s4QkcbTRsgT8Ju4O23gVpuh4Hy-dsHnb/view?usp=sharing", "https://drive.google.com/file/d/1eRoAWgYqNsmYHVa90J2Tm0ejIMa_8qvZ/view?usp=sharing", "https://drive.google.com/file/d/1r8SKClbsyAzYqOTfTEZXcQwf5YUEHPMG/view?usp=sharing", "https://drive.google.com/file/d/1L-Lc_Yv14Tlu3waA3ufDwd4kzTkZfLHC/view?usp=sharing", "https://drive.google.com/file/d/1bqz_eQkeo8Qc0OPaghIdQFMbDT9eFS77/view?usp=sharing", "https://drive.google.com/file/d/1VSSNBc76yh_OtyBzuC_S43nrFMIc8Fw1/view?usp=sharing", "https://drive.google.com/file/d/1NxQevabv6PPXxeXv5DZyeP8Q9GlHwD2r/view?usp=sharing", "https://drive.google.com/file/d/1S_XKeDtPI_UBupnIURZTPiddmLbjqnRZ/view?usp=sharing", "https://drive.google.com/file/d/1Bppf9VgUShBJWnYEw-4ZwDu5-A51nR9R/view?usp=sharing", "https://drive.google.com/file/d/1s7z4TpFmMpvx1-PpYsaukACOSTrkbcOf/view?usp=sharing", "https://drive.google.com/file/d/12Rrh0ixkQPvAAhExdnbMeol7c9aAzyTd/view?usp=sharing", "https://drive.google.com/file/d/1BGq_aacZjmkEfAa2Oyzfw32piEQfBAmZ/view?usp=sharing", "https://drive.google.com/file/d/1nxFqyFOa3C0TK8BLOYp-V6e_xjYGn0qV/view?usp=sharing", "https://drive.google.com/file/d/1xsMYVFufaM2gzMPKADNCrDbGyq0rhSV2/view?usp=sharing" ] # 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 extract text from a PDF #def extract_text_from_pdf(pdf_file_path): # pdf_reader = PdfReader(pdf_file_path) # 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, selected_appliance): # Filter documents based on appliance title filtered_documents = [doc for doc in vector_db.documents if doc.title == selected_appliance] context = "\n".join([doc.page_content for doc in filtered_documents]) # Interact with Groq API (unchanged) 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 # Streamlit app st.title("RAG-Based ChatBot (Already having Document)") st.write("Processing the Data links...") all_chunks = [] # Process each predefined Google Drive link for link in drive_links: try: # st.write(f"Processing link: {link}") # Download PDF pdf_stream = download_pdf_from_drive(link) # st.write("PDF Downloaded Successfully!") # Extract text text = extract_text_from_pdf(pdf_stream) # st.write("PDF Text Extracted Successfully!") # Chunk text chunks = chunk_text(text) # st.write(f"Created {len(chunks)} text chunks.") all_chunks.extend(chunks) except Exception as e: st.write(f"Error processing link {link}: {e}") if all_chunks: # Generate embeddings and store in FAISS vector_db = create_embeddings_and_store(all_chunks) st.write("Data is Ready Successfully!") # User query input user_query = st.text_input("Enter your query:") if user_query: response = query_vector_db(user_query, vector_db) st.write("Response from LLM:") st.write(response)