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| from pathlib import Path | |
| from llama_index import ServiceContext, download_loader, GPTVectorStoreIndex, LLMPredictor | |
| from llama_index import StorageContext, load_index_from_storage | |
| from langchain import OpenAI | |
| import gradio as gr | |
| import os | |
| # My personal OpenAI API key - do not misuse :P | |
| os.environ["OPENAI_API_KEY"] = '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' | |
| num_outputs = 512 | |
| llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.4, model_name="text-davinci-003", max_tokens=num_outputs)) | |
| service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) | |
| def construct_index(): | |
| SimpleCSVReader = download_loader("SimpleCSVReader") | |
| loader = SimpleCSVReader() | |
| docs = loader.load_data(file=Path('./docs/articles.csv')) | |
| index = GPTVectorStoreIndex.from_documents(docs, service_context=service_context) | |
| index.storage_context.persist() | |
| return index | |
| def chatbot(input_text): | |
| storage_context = StorageContext.from_defaults(persist_dir=Path("./storage/")) | |
| index = load_index_from_storage(storage_context, service_context=service_context) | |
| query_engine = index.as_query_engine(response_mode='compact') | |
| response = query_engine.query(input_text) | |
| return response.response | |
| # uncomment on first run, when generating an index | |
| index = construct_index() | |
| iface = gr.Interface(fn=chatbot, | |
| inputs=gr.inputs.Textbox(lines=7, label="Enter your text"), | |
| outputs="text", | |
| title="147 AI Chatbot") | |
| iface.launch() | |
| # with gr.Blocks() as demo: | |
| # chatbot = gr.Chatbot() | |
| # msg = gr.Textbox() | |
| # clear = gr.Button("Clear") | |
| # def respond(message, chat_history): | |
| # storage_context = StorageContext.from_defaults(persist_dir=Path("./storage/")) | |
| # index = load_index_from_storage(storage_context, service_context=service_context) | |
| # query_engine = index.as_query_engine(response_mode='compact') | |
| # response = query_engine.query(message) | |
| # chat_history.append((message, response)) | |
| # return "", chat_history | |
| # msg.submit(respond, [msg, chatbot], [msg, chatbot]) | |
| # clear.click(lambda: None, None, chatbot, queue=False) | |
| # demo.launch() | |