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| ########################### | |
| # 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 #################### | |
| def process_transcripts(files, context, session_key): | |
| print(session_key) | |
| with EmbeddingService(conf, | |
| pinecone=pc_connector, | |
| session_key=session_key) as e: | |
| f = e.run(files) | |
| return "Completed Loading Data" | |
| def retrieve_answer(question, goals, session_key): | |
| print(session_key) | |
| with QAService(conf, | |
| pinecone=pc_connector, | |
| model_pipeline=pipelines, | |
| question=question, | |
| goals=goals, | |
| session_key=session_key) as q: | |
| f, c = q.run() | |
| return f, c | |
| def drop_namespace(arg): | |
| print("Deleted namespace: " + arg) | |
| def generate_key(): | |
| unique_key = str(uuid.uuid1()) | |
| unique_key = 'User_' + unique_key | |
| timer = threading.Timer(10, drop_namespace, [unique_key]) | |
| timer.start() | |
| # generate new namespace in pinecone | |
| 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) | |