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import gradio as gr
from commons.Model import model
from commons.Configs import configs
from commons.OpenAIClient import openaiClient
from prepareutils.Dataset import dataset
import numpy as np
import openai
# load the model once
clf = model.load()
# load dataset
qaDataset = dataset.loadDataset()
def predict(question, openaiKey):
# set openaiKey
configs.OPENAI_KEY = openaiKey
openai.api_key = openaiKey
# embed question
questionEmbedding = openaiClient.generateEmbeddings([question])[0]
# predict answer index
answerIndex = clf.predict([questionEmbedding]).item()
# get answer
bestAnswer = qaDataset[answerIndex]
return bestAnswer["answer"]
def randomExamples(numberOfExamples=15):
# create random indexes in the range between 0 and len(qaDataset)
randomIndexes = np.random.randint(0, len(qaDataset), numberOfExamples)
examples = []
for index in randomIndexes:
question = qaDataset[index]["question"]
examples.append([question])
return examples
gr.Interface.load(
"models/giovannefeitosa/chatbot-about-pele",
fn=predict,
inputs=[gr.Textbox(label="Question", lines=2),
gr.Textbox(label="OpenAI Key")],
outputs=gr.Textbox(label="Answer", lines=2),
examples=randomExamples(),
).launch()
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