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	import gradio as gr
	from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
	import torch
	import theme

	theme = theme.Theme()

	import os
	import sys
	sys.path.append('../..')

	#langchain
	from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
	from langchain.embeddings import HuggingFaceEmbeddings
	from langchain.prompts import PromptTemplate
	from langchain.chains import RetrievalQA
	from langchain.prompts import ChatPromptTemplate
	from langchain.schema import StrOutputParser
	from langchain.schema.runnable import Runnable
	from langchain.schema.runnable.config import RunnableConfig
	from langchain.chains import (
	LLMChain, ConversationalRetrievalChain)
	from langchain.vectorstores import Chroma
	from langchain.memory import ConversationBufferMemory
	from langchain.chains import LLMChain
	from langchain.prompts.prompt import PromptTemplate
	from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate
	from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, MessagesPlaceholder
	from langchain.document_loaders import PyPDFDirectoryLoader
	from pydantic import BaseModel, Field
	from langchain.output_parsers import PydanticOutputParser
	from langchain_community.llms import HuggingFaceHub
	from langchain_community.document_loaders import WebBaseLoader

	from pydantic import BaseModel
	import shutil



	custom_title = "<span style='color: rgb(243, 239, 224);'>Green Greta</span>"


	# Cell 1: Image Classification Model
	image_pipeline = pipeline(task="image-classification", model="guillen/vit-basura-test1")

	def predict_image(input_img):
	predictions = image_pipeline(input_img)
	return {p["label"]: p["score"] for p in predictions}

	image_gradio_app = gr.Interface(
	fn=predict_image,
	inputs=gr.Image(label="Image", sources=['upload', 'webcam'], type="pil"),
	outputs=[gr.Label(label="Result")],
	title=custom_title,
	theme=theme
	)

	loader = WebBaseLoader(["https://www.epa.gov/recycle/frequent-questions-recycling", "https://www.whitehorsedc.gov.uk/vale-of-white-horse-district-council/recycling-rubbish-and-waste/lets-get-real-about-recycling/", "https://www.teimas.com/blog/13-preguntas-y-respuestas-sobre-la-ley-de-residuos-07-2022", "https://www.molok.com/es/blog/gestion-de-residuos-solidos-urbanos-rsu-10-dudas-comunes"])
	data=loader.load()
	# split documents
	text_splitter = RecursiveCharacterTextSplitter(
	chunk_size=1024,
	chunk_overlap=150,
	length_function=len
	)
	docs = text_splitter.split_documents(data)
	# define embedding
	embeddings = HuggingFaceEmbeddings(model_name='thenlper/gte-small')
	# create vector database from data
	persist_directory = 'docs/chroma/'

	# Remove old database files if any
	shutil.rmtree(persist_directory, ignore_errors=True)
	vectordb = Chroma.from_documents(
	documents=docs,
	embedding=embeddings,
	persist_directory=persist_directory
	)
	# define retriever
	retriever = vectordb.as_retriever(search_kwargs={"k": 2}, search_type="mmr")

	class FinalAnswer(BaseModel):
	question: str = Field(description="the original question")
	answer: str = Field(description="the extracted answer")

	# Assuming you have a parser for the FinalAnswer class
	parser = PydanticOutputParser(pydantic_object=FinalAnswer)

	template = """
	Your name is Greta and you are a recycling chatbot with the objective to anwer questions from user in English or Spanish /
	Use the following pieces of context to answer the question /
	If the question is English answer in English /
	If the question is Spanish answer in Spanish /
	Do not mention the word context when you answer a question /
	Answer the question fully and provide as much relevant detail as possible. Do not cut your response short /
	Context: {context}
	User: {question}
	{format_instructions}
	"""

	# Create the chat prompt templates
	sys_prompt = SystemMessagePromptTemplate.from_template(template)
	qa_prompt = ChatPromptTemplate(
	messages=[
	sys_prompt,
	HumanMessagePromptTemplate.from_template("{question}")],
	partial_variables={"format_instructions": parser.get_format_instructions()}
	)
	llm = HuggingFaceHub(
	repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
	task="text-generation",
	model_kwargs={
	"max_new_tokens": 2000,
	"top_k": 30,
	"temperature": 0.1,
	"repetition_penalty": 1.03
	},
	)

	qa_chain = ConversationalRetrievalChain.from_llm(
	llm = llm,
	memory = ConversationBufferMemory(llm=llm, memory_key="chat_history", input_key='question', output_key='output'),
	retriever = retriever,
	verbose = True,
	combine_docs_chain_kwargs={'prompt': qa_prompt},
	get_chat_history = lambda h : h,
	rephrase_question = False,
	output_key = 'output',
	)

	def chat_interface(question,history):
	result = qa_chain.invoke({'question': question})
	output_string = result['output']

	# Find the index of the last occurrence of "answer": in the string
	answer_index = output_string.rfind('"answer":')

	# Extract the substring starting from the "answer": index
	answer_part = output_string[answer_index + len('"answer":'):].strip()

	# Find the next occurrence of a double quote to get the start of the answer value
	quote_index = answer_part.find('"')

	# Extract the answer value between double quotes
	answer_value = answer_part[quote_index + 1:answer_part.find('"', quote_index + 1)]

	return answer_value


	chatbot_gradio_app = gr.ChatInterface(
	fn=chat_interface,
	title=custom_title
	)

	# Combine both interfaces into a single app
	app = gr.TabbedInterface(
	[image_gradio_app, chatbot_gradio_app],
	tab_names=["Green Greta Image Classification","Green Greta Chat"],
	theme=theme
	)

	app.queue()
	app.launch()