app.py

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="Mercedes-Benz S-Class Owner's Manual",
        description="Ask your vehicle's manual questions and get answers",
        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()