96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
import csv
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import queue
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import threading
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import streamlit as st
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from embedchain import Pipeline as App
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from embedchain.config import BaseLlmConfig
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from embedchain.helpers.callbacks import (StreamingStdOutCallbackHandlerYield,
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generate)
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@st.cache_resource
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def sadhguru_ai():
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app = App()
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return app
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# Function to read the CSV file row by row
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def read_csv_row_by_row(file_path):
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with open(file_path, mode="r", newline="", encoding="utf-8") as file:
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csv_reader = csv.DictReader(file)
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for row in csv_reader:
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yield row
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@st.cache_resource
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def add_data_to_app():
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app = sadhguru_ai()
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file_path = "data.csv"
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for row in read_csv_row_by_row(file_path):
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app.add(row["url"], data_type="web_page")
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app = sadhguru_ai()
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add_data_to_app()
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assistant_avatar_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Sadhguru-Jaggi-Vasudev.jpg/640px-Sadhguru-Jaggi-Vasudev.jpg" # noqa: E501
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st.title("🙏 Sadhguru AI")
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styled_caption = '<p style="font-size: 17px; color: #aaa;">🚀 An <a href="https://github.com/embedchain/embedchain">Embedchain</a> app powered with Sadhguru\'s wisdom!</p>' # noqa: E501
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st.markdown(styled_caption, unsafe_allow_html=True) # noqa: E501
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if "messages" not in st.session_state:
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st.session_state.messages = [
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{
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"role": "assistant",
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"content": """
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Hi, I'm Sadhguru AI! I'm a mystic, yogi, visionary, and spiritual master. I'm here to answer your questions about life, the universe, and everything.
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""", # noqa: E501
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}
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]
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for message in st.session_state.messages:
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role = message["role"]
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with st.chat_message(role, avatar=assistant_avatar_url if role == "assistant" else None):
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st.markdown(message["content"])
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if prompt := st.chat_input("Ask me anything!"):
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with st.chat_message("user"):
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st.markdown(prompt)
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st.session_state.messages.append({"role": "user", "content": prompt})
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with st.chat_message("assistant", avatar=assistant_avatar_url):
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msg_placeholder = st.empty()
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msg_placeholder.markdown("Thinking...")
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full_response = ""
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q = queue.Queue()
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def app_response(result):
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config = BaseLlmConfig(stream=True, callbacks=[StreamingStdOutCallbackHandlerYield(q)])
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answer, citations = app.chat(prompt, config=config, citations=True)
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result["answer"] = answer
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result["citations"] = citations
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results = {}
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thread = threading.Thread(target=app_response, args=(results,))
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thread.start()
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for answer_chunk in generate(q):
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full_response += answer_chunk
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msg_placeholder.markdown(full_response)
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thread.join()
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answer, citations = results["answer"], results["citations"]
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if citations:
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full_response += "\n\n**Sources**:\n"
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for i, citations in enumerate(citations):
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full_response += f"{i+1}. {citations[1]}\n"
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msg_placeholder.markdown(full_response)
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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