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| import os | |
| from embedchain import App | |
| import streamlit as st | |
| config = { | |
| 'llm': { | |
| 'provider': 'huggingface', | |
| 'config': { | |
| 'model': 'mistralai/Mistral-7B-Instruct-v0.2', | |
| 'max_tokens': 200, | |
| 'top_p': 0.5 | |
| } | |
| }, | |
| 'embedder': { | |
| 'provider': 'huggingface', | |
| 'config': { | |
| 'model': 'sentence-transformers/all-mpnet-base-v2' | |
| } | |
| } | |
| } | |
| with st.sidebar: | |
| huggingface_access_token = st.text_input("Hugging face Token", key="chatbot_api_key", type="password") | |
| "[Get Hugging Face Access Token](https://huggingface.co/settings/tokens)" | |
| "[View the source code](https://github.com/embedchain/examples/mistral-streamlit)" | |
| st.title("💬 Chatbot") | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [ | |
| { | |
| "role": "assistant", | |
| "content": """ | |
| Hi! I'm a chatbot. I can answer your questions based on IPL Wiki Page""", | |
| } | |
| ] | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| if prompt := st.chat_input("Ask me anything!"): | |
| if not st.session_state.chatbot_api_key: | |
| st.error("Please enter your Hugging Face Access Token") | |
| st.stop() | |
| os.environ["HUGGINGFACE_ACCESS_TOKEN"] = st.session_state.chatbot_api_key | |
| app = App.from_config(config = config) | |
| app.add("https://en.wikipedia.org/wiki/Indian_Premier_League") | |
| if prompt.startswith("/add"): | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| prompt = prompt.replace("/add", "").strip() | |
| with st.chat_message("assistant"): | |
| message_placeholder = st.empty() | |
| message_placeholder.markdown("Adding to knowledge base...") | |
| app.add(prompt) | |
| message_placeholder.markdown(f"Added {prompt} to knowledge base!") | |
| st.session_state.messages.append({"role": "assistant", "content": f"Added {prompt} to knowledge base!"}) | |
| st.stop() | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("assistant"): | |
| msg_placeholder = st.empty() | |
| msg_placeholder.markdown("Thinking...") | |
| full_response = "" | |
| full_response_string = [] | |
| for response in app.chat(prompt, citations = False): | |
| msg_placeholder.empty() | |
| full_response += response | |
| full_response_string.clear() | |
| full_response_string = full_response.rsplit('Answer:', 1) | |
| msg_placeholder.markdown(full_response_string[1]) | |
| st.session_state.messages.append({"role": "assistant", "content": full_response_string[1]}) |