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import gradio as gr
import os
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Configuration
DOCS_DIR = "business_docs"
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
# Initialize components once at startup
def initialize_system():
# Load and process PDFs from business_docs folder
if not os.path.exists(DOCS_DIR):
raise FileNotFoundError(f"Business documents folder '{DOCS_DIR}' not found")
pdf_files = [os.path.join(DOCS_DIR, f) for f in os.listdir(DOCS_DIR) if f.endswith(".pdf")]
if not pdf_files:
raise ValueError(f"No PDF files found in {DOCS_DIR} folder")
# Process documents
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
texts = []
for pdf in pdf_files:
loader = PyPDFLoader(pdf)
pages = loader.load_and_split(text_splitter)
texts.extend(pages)
# Create vector store
embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
vector_store = FAISS.from_documents(texts, embeddings)
# Load model with quantization for faster inference
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
device_map="auto",
load_in_8bit=True
)
return vector_store, model, tokenizer
# Initialize system components
try:
vector_store, model, tokenizer = initialize_system()
print("System initialized successfully with business documents")
except Exception as e:
print(f"Initialization error: {str(e)}")
raise
# Response generation with context
def generate_response(query):
# Retrieve relevant context
docs = vector_store.similarity_search(query, k=3)
context = "\n".join([doc.page_content for doc in docs])
# Create instruction prompt
prompt = f"""<s>[INST] You are a customer support agent.
Answer ONLY using information from the provided business documents.
If unsure, say "I don't have information about that."
Context: {context}
Question: {query} [/INST]"""
# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=500,
temperature=0.3,
do_sample=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[-1].strip()
# Chat interface
with gr.Blocks() as demo:
gr.Markdown("## Business Support Chatbot\nAsk questions about our services!")
chatbot = gr.Chatbot(label="Conversation")
msg = gr.Textbox(label="Type your question")
clear = gr.Button("Clear History")
def respond(message, chat_history):
try:
response = generate_response(message)
except Exception as e:
response = "Sorry, I'm having trouble answering right now. Please try again later."
chat_history.append((message, response))
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch() |