Update app.py

app.py

@@ -1,24 +1,25 @@
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import os
+from transformers import pipeline
+import torch
+
 hf_token = os.getenv('HF_API_TOKEN')
-import streamlit as st
 
-from transformers import pipeline
 
-# Load the model
-generator = pipeline("text-generation", model="meta-llama/Meta-Llama-3.1-8B", token= hf_token)
 
-# Create an API route in Streamlit
-@st.cache_resource
-def predict(inputs):
-    return generator(inputs, max_length=512, top_p=0.9, temperature=0.8)[0]['generated_text']
+# Load the Llama 3.1 model and tokenizer
+model_name = "meta-llama/Meta-Llama-3.1-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name, token= hf_token)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto", token= hf_token)
 
-@st.cache_resource
-def predict_endpoint():
-    inputs = st.experimental_get_query_params().get('inputs', [''])[0]
-    return predict(inputs)
+# Streamlit app interface
+st.title("Llama 3.1 Text Generator")
+prompt = st.text_area("Enter a prompt:", "Once upon a time")
 
-st.experimental_set_query_params(result=predict_endpoint())
+if st.button("Generate"):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_length=512, top_p=0.9, temperature=0.8)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    st.write(generated_text)
 
-st.title("Llama3.1 API is Running")