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