| from llama_index import Document, SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext | |
| from llama_index import download_loader | |
| from langchain import OpenAI | |
| from pathlib import Path | |
| import gradio as gr | |
| import sys | |
| import os | |
| dataFiles = ["RetroMay", "SupplierScoreCardWithGroup3", "Agreement","RetroApril","RetroMarch", "Snowflake", "Datadog", "Databricks", "SplunkProducts", "SplunkEnterprise"] | |
| cache = {} | |
| def indexFile(filePath): | |
| PandasCSVReader = download_loader("PandasCSVReader") | |
| loader = PandasCSVReader() | |
| documents = loader.load_data(file=Path('./csv/' + filePath + '.csv')) | |
| index = GPTSimpleVectorIndex.from_documents(documents) | |
| index.save_to_disk("index/" + filePath + '.json') | |
| def loadData(): | |
| """ | |
| Load indices from disk for improved performance | |
| """ | |
| for file in dataFiles : | |
| print("Loading file "+ file) | |
| indexFilePath= "index/" + file + '.json' | |
| if not os.path.exists(indexFilePath): | |
| indexFile(file) | |
| cache[file]= GPTSimpleVectorIndex.load_from_disk(indexFilePath) | |
| 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 | |
| loadData() | |
| iface = gr.Interface(fn=chatbot, | |
| inputs= [ | |
| gr.Dropdown(dataFiles, | |
| type="value", value="Agreement", 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) |