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from InnovationHub.llm.vector_store import *
from InnovationHub.llm.chain import *
import os
import pprint
import codecs
import chardet
import gradio as gr
from langchain.llms import HuggingFacePipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
from langchain.chains.conversation.memory import ConversationalBufferWindowMemory
from EdgeGPT import Chatbot
"""
# Create the vector index
db_path = "./data/s-class-manual"
embeddings = HuggingfaceEmbeddings()
index = FAISS(docs=docs, folder_path=db_path, embeddings=embeddings)
"""
# Launch the Gradio UI
def start_gradio():
chatbot_interface = gr.Interface(
fn=chatbot,
inputs=["text", gr.inputs.Checkbox(label="Create bot"), gr.inputs.Slider(
minimum=1, maximum=10, step=1, label="k")],
outputs="text",
title="Owner's Manual",
description="Ask your vehicle manual and get a response.",
examples=[
["What are the different features of the dashboard console?", True, 2],
["What do they do?", False, 3]
]
)
chatbot_interface.launch()
if __name__ == '__main__':
start_ui()
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