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| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| from huggingface_hub import loginimport streamlit as st | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| st.title("📚 AI Adaptive Learning (Local LLaMA)") | |
| # Hugging Face authentication for private models | |
| HF_API_TOKEN = st.secrets["HF_API_TOKEN"] | |
| login(token=HF_API_TOKEN) | |
| MODEL_ID = "meta-llama/Llama-2-7b-chat-hf" # or your private model | |
| def load_model(): | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_auth_token=HF_API_TOKEN) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL_ID, | |
| use_auth_token=HF_API_TOKEN, | |
| device_map="auto", # uses GPU if available, CPU otherwise | |
| ) | |
| return tokenizer, model | |
| tokenizer, model = load_model() | |
| # Load model & tokenizer | |
| MODEL_ID = "TheBloke/vicuna-7B-1.1-HF" # smaller public LLaMA-like model | |
| # caches model to avoid reload on every run | |
| def load_model(): | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL_ID, | |
| device_map="auto", # automatically uses CPU or GPU | |
| ) | |
| return tokenizer, model | |
| tokenizer, model = load_model() | |
| # Input question | |
| user_input = st.text_input("Ask a question:") | |
| # Generate answer locally | |
| if st.button("Submit") and user_input: | |
| inputs = tokenizer(user_input, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=256) | |
| answer = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| st.subheader("AI Answer:") | |
| st.write(answer) | |
| user_input = st.text_input("Ask a question:") | |