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| from fastapi import FastAPI | |
| import gradio as gr | |
| from gradio.themes.base import Base | |
| from innovation_pathfinder_ai.agents.hf_mixtral_agent import agent_executor | |
| from innovation_pathfinder_ai.source_container.container import ( | |
| all_sources | |
| ) | |
| from innovation_pathfinder_ai.utils.utils import extract_urls | |
| from innovation_pathfinder_ai.utils import logger | |
| from innovation_pathfinder_ai.utils.utils import ( | |
| generate_uuid | |
| ) | |
| from langchain_community.vectorstores import Chroma | |
| import chromadb | |
| import dotenv | |
| import os | |
| dotenv.load_dotenv() | |
| persist_directory = os.getenv('VECTOR_DATABASE_LOCATION') | |
| logger = logger.get_console_logger("app") | |
| app = FastAPI() | |
| def initialize_chroma_db() -> Chroma: | |
| collection_name = os.getenv('CONVERSATION_COLLECTION_NAME') | |
| client = chromadb.PersistentClient( | |
| path=persist_directory | |
| ) | |
| collection = client.get_or_create_collection( | |
| name=collection_name, | |
| ) | |
| return collection | |
| if __name__ == "__main__": | |
| db = initialize_chroma_db() | |
| def add_text(history, text): | |
| history = history + [(text, None)] | |
| return history, "" | |
| def bot(history): | |
| response = infer(history[-1][0], history) | |
| sources = extract_urls(all_sources) | |
| src_list = '\n'.join(sources) | |
| current_id = generate_uuid() | |
| db.add( | |
| ids=[current_id], | |
| documents=[response['output']], | |
| metadatas=[ | |
| { | |
| "human_message":history[-1][0], | |
| "sources": 'Internal Knowledge Base From: \n\n' + src_list | |
| } | |
| ] | |
| ) | |
| if not sources: | |
| response_w_sources = response['output']+"\n\n\n Sources: \n\n\n Internal knowledge base" | |
| else: | |
| response_w_sources = response['output']+"\n\n\n Sources: \n\n\n"+src_list | |
| history[-1][1] = response_w_sources | |
| all_sources.clear() | |
| return history | |
| def infer(question, history): | |
| query = question | |
| result = agent_executor.invoke( | |
| { | |
| "input": question, | |
| "chat_history": history | |
| } | |
| ) | |
| return result | |
| def vote(data: gr.LikeData): | |
| if data.liked: | |
| print("You upvoted this response: " + data.value) | |
| else: | |
| print("You downvoted this response: " + data.value) | |
| css=""" | |
| #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} | |
| """ | |
| title = """ | |
| <div style="text-align:left;"> | |
| <p>Hello Human, I am your AI knowledge research assistant. I can explore topics across ArXiv, Wikipedia and use Google search.<br /> | |
| </div> | |
| """ | |
| with gr.Blocks(theme=gr.themes.Soft(), title="AlfredAI - AI Knowledge Research Assistant") as demo: | |
| # with gr.Tab("Google|Wikipedia|Arxiv"): | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML(title) | |
| with gr.Row(): | |
| question = gr.Textbox(label="Question", | |
| placeholder="Type your question and hit Enter",) | |
| chatbot = gr.Chatbot([], | |
| elem_id="AI Assistant", | |
| bubble_full_width=False, | |
| avatar_images=(None, "./innovation_pathfinder_ai/assets/avatar.png"), | |
| height=480,) | |
| chatbot.like(vote, None, None) | |
| clear = gr.Button("Clear") | |
| question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then( | |
| bot, chatbot, chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| with gr.Accordion("Open for More!", open=False): | |
| gr.Markdown("Nothing yet...") | |
| demo.queue() | |
| demo.launch(debug=True, favicon_path="innovation_pathfinder_ai/assets/favicon.ico", share=True) | |
| x = 0 # for debugging purposes | |
| app = gr.mount_gradio_app(app, demo, path="/") |