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from gpt_index import Document, SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper |
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from langchain import OpenAI |
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import openai |
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import gradio as gr |
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import sys |
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import os |
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def chatbot(indexName, input_text): |
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""" |
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Chatbot function that takes in a prompt and returns a response |
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""" |
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index = GPTSimpleVectorIndex.load_from_disk(indexName+'.json') |
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response = index.query(input_text, response_mode="compact") |
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return response.response |
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iface = gr.Interface(fn=chatbot, |
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inputs= [ |
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gr.Dropdown(["RetroFeedback", "WizFeedback", "Passwordless"], |
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type="value", value="RetroFeedback", label="Select Pulse Data"), |
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gr.Textbox(lines=7, label="Ask any question", placeholder='What is the summary?')], |
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outputs="text", |
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title="NLP Demo for Chat Interface") |
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iface.launch(auth=('axiamatic', os.environ['LOGIN_PASS']), |
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auth_message='For access, please check my Slack profile or contact me in Slack.', |
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share=False) |