########################### # UI for Meeting RAG Q&A. # ########################### ##################### Imports ##################### import uuid import threading import gradio as gr import spaces from utilities.setup import get_files from connections.pinecone import PineconeConnector from connections.model import InferencePipeline from services.embed_service.embed import EmbeddingService from services.qa_service.qna import QAService #################### Functions #################### @spaces.GPU def process_transcripts(files, context, session_key): with EmbeddingService(conf, pinecone=pc_connector, session_key=session_key) as e: f = e.run(files) return "Completed Loading Data" @spaces.GPU def retrieve_answer(question, goals, session_key): keycheck = namespace_check(session_key) with QAService(conf, pinecone=pc_connector, model_pipeline=pipelines, question=question, goals=goals, session_key=session_key, keycheck=keycheck) as q: f, c = q.run() return f, c def namespace_check(arg): index = pc_connector.Index(conf["embeddings"]["index_name"]) stats = index.DescribeIndexStats() name_list = stats['namespaces'].keys() arg in name_list return arg in name_list def drop_namespace(arg): if conf["embeddings"]["override"]: pass print("Maintained Namespace: " + conf["embeddings"]["demo_namespace"]) else: namecheck = namespace(arg) if namecheck: pc_connector.delete(namespace=arg, delete_all=True) print("Deleted namespace: " + arg) def generate_key(): unique_key = str(uuid.uuid1()) unique_key = 'User_' + unique_key timer = threading.Timer(100, drop_namespace, [unique_key]) timer.start() return unique_key def b_clicked(o): return gr.Button(interactive=True) ##################### Process ##################### def main(conf): with gr.Blocks() as demo: # Main page with gr.TabItem(conf["layout"]["page_names"][0]): gr.Markdown(get_files.load_markdown_file(conf["layout"]["about"])) # User config page with gr.TabItem(conf["layout"]["page_names"][1]): gr.Markdown("# Your User Configurations") gr.Markdown("**2 Options:**") gr.Markdown("""1. Generate a unique key to upload your personal transcripts. Copy this key to use in the next page. Your documents will be queryable for 1 hour after generation.""") gr.Markdown("""2. Or, go straight to the next tab to just ask your question to the meetings that are already included!""") create_unique_key = gr.Button("Generate unique key") output_unique_key = gr.Textbox(label="Your session key", interactive=True , show_copy_button=True, show_label=True) create_unique_key.click(fn=generate_key, outputs=output_unique_key) ### This should not be visible until key is generated. load_file = gr.UploadButton(label="Upload Transcript (.vtt)", file_types=[".vtt"], file_count='multiple', interactive=False) repository = gr.Textbox(label="Progress", value="Waiting for load...", visible=True) gr.Markdown("## Additional context you want to provide?") gr.Markdown("Try to keep this portion as concise as possible.") goals = gr.Textbox(label="Analysis Goals", value=conf["defaults"]["goals"]) # not incorporated yet. Will be with Q&A. load_file.upload(process_transcripts, [load_file, goals, output_unique_key], repository) create_unique_key.click(fn=b_clicked, inputs=create_unique_key, outputs=load_file) # Meeting Question & Answer Page with gr.TabItem(conf["layout"]["page_names"][2]): session_key = gr.Textbox(label="Paste Session key here.", value="") question = gr.Textbox(label="Ask a Question", value=conf["defaults"]["question"]) ask_button = gr.Button("Ask!") model_output = gr.Markdown("### Answer") context_output = gr.components.Textbox(label="Retrieved Context") ask_button.click(fn=retrieve_answer, inputs=[question, goals, session_key], outputs=[model_output,context_output]) demo.launch() ##################### Execute ##################### if __name__ == "__main__": # Get config conf = get_files.json_cfg() # Get keys keys = get_files.get_keys() # initialize pinecone connector pc_connector = PineconeConnector( api_key=keys["pinecone"], index_name=conf["embeddings"]["index_name"], embedding=conf["embeddings"]["embedding"], ) # initialize model connector pipelines = InferencePipeline(conf, api_key=keys["huggingface"] ) # run main main(conf)