| from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader | |
| import os | |
| from llama_index.node_parser import SimpleNodeParser | |
| from llama_index import StorageContext, load_index_from_storage | |
| import gradio as gr | |
| import openai | |
| os.environ['OPENAI_API_KEY'] = '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' | |
| # rebuild storage context | |
| storage_context = StorageContext.from_defaults(persist_dir="index_dir") | |
| # load index | |
| index = load_index_from_storage(storage_context) | |
| # strat a search engine | |
| query_engine = index.as_query_engine() | |
| # APP | |
| # def get_model_reply_no_prev_context(question): | |
| # response = query_engine.query(question) | |
| # final_response = response.response[1:] | |
| # return final_response | |
| # def get_model_reply_no_prev_context(question): | |
| # final_response = question | |
| # return final_response | |
| # title = "Knowledge Center at Penta Building Group" | |
| # description = """ | |
| # The program is trained to answer questions based on the documentation of 'Lessons Learned' from previous projects! | |
| # """ | |
| # article = "Your feedback matters!If you like it, contact me at mgupta70@asu.edu" | |
| # gr.Interface( | |
| # fn=get_model_reply_no_prev_context, | |
| # inputs="textbox", | |
| # outputs="text", | |
| # title=title, | |
| # description=description, | |
| # article=article, | |
| # examples=[["Which code is to be used while planning a pedestrian walkway?"], ["What is AHJ?"]], live=True | |
| # ).launch() | |
| import gradio as gr | |
| def sketch_recognition(img): | |
| pass# Implement your sketch recognition model here... | |
| gr.Interface(fn=sketch_recognition, inputs="sketchpad", outputs="label").launch() | |