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| import numpy as np | |
| import streamlit as st | |
| from openai import OpenAI | |
| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Initialize the OpenAI client | |
| client = OpenAI( | |
| base_url="https://api-inference.huggingface.co/v1", | |
| api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN') # Replace with your token | |
| ) | |
| # Create supported model | |
| model_links = { | |
| "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct" | |
| } | |
| # Pull info about the model to display | |
| model_info = { | |
| "Meta-Llama-3-8B": { | |
| 'description': """The Llama (3) model is a **Large Language Model (LLM)** designed to assist with question and answer interactions.\n | |
| \nThis model was created by Meta's AI team and has over 8 billion parameters.\n | |
| **Training**: The model was fine-tuned on science textbooks from the NCERT curriculum using Docker AutoTrain to ensure it can provide relevant and accurate responses in the education domain.\n | |
| **Purpose**: This version of Llama has been trained specifically for educational purposes, focusing on answering science-related queries in a clear and simple manner to help students and teachers alike.\n""" | |
| } | |
| } | |
| # Reset the conversation | |
| def reset_conversation(): | |
| st.session_state.conversation = [] | |
| st.session_state.messages = [] | |
| return None | |
| # App title and description | |
| st.title("Sci-Mom 👩🏫 ") | |
| st.subheader("AI chatbot for Solving your doubts 📚 :)") | |
| # Custom description for SciMom in the sidebar | |
| st.sidebar.write("Built for my mom, with love ❤️. This model is pretrained with textbooks of Science NCERT.") | |
| st.sidebar.write("Base-Model used: Meta Llama, trained using: Docker AutoTrain.") | |
| # Add technical details in the sidebar | |
| st.sidebar.markdown(model_info["Meta-Llama-3-8B"]['description']) | |
| st.sidebar.markdown("*By Gokulnath ♔ *") | |
| # If model selection was needed (now removed) | |
| selected_model = "Meta-Llama-3-8B" # Only one model remains | |
| if "prev_option" not in st.session_state: | |
| st.session_state.prev_option = selected_model | |
| if st.session_state.prev_option != selected_model: | |
| st.session_state.messages = [] | |
| st.session_state.prev_option = selected_model | |
| reset_conversation() | |
| # Pull in the model we want to use | |
| repo_id = model_links[selected_model] | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Accept user input | |
| if prompt := st.chat_input("Ask Scimom!"): | |
| # Display user message in chat message container | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| try: | |
| stream = client.chat.completions.create( | |
| model=model_links[selected_model], | |
| messages=[ | |
| {"role": m["role"], "content": m["content"]} | |
| for m in st.session_state.messages | |
| ], | |
| temperature=0.5, # Default temperature setting | |
| stream=True, | |
| max_tokens=3000, | |
| ) | |
| response = st.write_stream(stream) | |
| except Exception as e: | |
| response = "😵💫 Something went wrong. Please try again later." | |
| st.write(response) | |
| st.write("This was the error message:") | |
| st.write(e) | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |