Update app.py

app.py

@@ -1,9 +1,28 @@
 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
+
+@st.cache_resource
+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
 @st.cache_resource  # caches model to avoid reload on every run
@@ -26,4 +45,5 @@ if st.button("Submit") and user_input:
     outputs = model.generate(**inputs, max_new_tokens=256)
     answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
     st.subheader("AI Answer:")
-    st.write(answer)
+    st.write(answer)
+    user_input = st.text_input("Ask a question:")