Update app.py

app.py

@@ -1,19 +1,43 @@
 import streamlit as st
-import sys
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel, PeftConfig
+from huggingface_hub import login
 
-st.write("Python version:", sys.version)
-st.write("Starting app...")
+# Set your HuggingFace token
+hf_token = st.secrets["HF_TOKEN25"]  # Using Streamlit secrets
+try:
+    login(token=hf_token)
+    st.success("Successfully logged in to Hugging Face!")
+except Exception as e:
+    st.error(f"Error logging in to Hugging Face: {str(e)}")
 
 st.title("LLaMA Chatbot")
-st.write("Status: Basic app structure loaded")
 
-# Test imports to verify installation
-try:
-    import torch
-    import transformers
-    import peft
-    st.success("✅ All required packages are installed!")
-    st.write("Torch version:", torch.__version__)
-    st.write("Transformers version:", transformers.__version__)
-except Exception as e:
-    st.error(f"❌ Error loading packages: {str(e)}")
+@st.cache_resource
+def load_model():
+    try:
+        model_path = "Alaaeldin/llama2-app"
+        st.info("Loading model... This might take a minute.")
+        
+        tokenizer = AutoTokenizer.from_pretrained(model_path, token=hf_token)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            torch_dtype=torch.float16,
+            device_map="auto",
+            load_in_8bit=True,
+            token=hf_token
+        )
+        st.success("✅ Model loaded successfully!")
+        return model, tokenizer
+    except Exception as e:
+        st.error(f"❌ Error loading model: {str(e)}")
+        return None, None
+
+model, tokenizer = load_model()
+
+# Add a text input
+if model and tokenizer:
+    user_input = st.text_input("Your message:", "")
+    if st.button("Send"):
+        st.write("User:", user_input)