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from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext, StorageContext, load_index_from_storage
from langchain import OpenAI
import gradio
import os
os.environ["OPENAI_API_KEY"] = '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'
def construct_index(directory_path):
# set number of output tokens
num_outputs = 256
_llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="gpt-4", max_tokens=num_outputs))
service_context = ServiceContext.from_defaults(llm_predictor=_llm_predictor)
docs = SimpleDirectoryReader(directory_path).load_data()
index = GPTVectorStoreIndex.from_documents(docs, service_context=service_context)
#Directory in which the indexes will be stored
index.storage_context.persist(persist_dir="indexes")
return index
def chatbot(input_text):
# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="indexes")
#load indexes from directory using storage_context
query_engne = load_index_from_storage(storage_context).as_query_engine()
response = query_engne.query(input_text)
#returning the response
return response.response
#Creating the web UIusing gradio
iface = gradio.Interface(fn=chatbot,
inputs=gradio.inputs.Textbox(lines=4, label="Enter your question here"),
outputs=gradio.outputs.Textbox(label="Generated Text"),
title="My Custom-trained AI Chatbot")
#Constructing indexes based on the documents in trainingData folder
#This can be skipped if you have already trained your app and need to re-run it
index = construct_index("trainingData")
#launching the web UI using gradio
iface.launch()
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