Create app.py

app.py

@@ -0,0 +1,26 @@
+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
+    model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
+    return pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Load the text generation pipeline
+text_gen_pipeline = load_model()
+
+# Streamlit app interface
+st.title("Text Generation with Meta-Llama-3-8B")
+st.write("Enter some text and click the button to generate a continuation.")
+
+# User input
+user_input = st.text_area("Input Text", "Once upon a time")
+
+# Generate text on button click
+if st.button("Generate Text"):
+    with st.spinner("Generating..."):
+        generated_text = text_gen_pipeline(user_input, max_length=100, num_return_sequences=1)
+        st.write(generated_text[0]['generated_text'])
+