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