Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "norman-codes/transfer-learning-attempt1"
+
+# Load the model and tokenizer from Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+st.title("Text Generation with GPT-Neo")
+st.write("This is a text generation model running on Hugging Face Spaces. Enter a prompt to generate text.")
+
+prompt = st.text_input("Enter your prompt here:")
+
+if st.button("Generate Text"):
+    with st.spinner("Generating..."):
+        # Encode the input prompt and generate text
+        input_ids = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids
+        generated_ids = model.generate(input_ids, max_length=100)
+        generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    st.text_area("Generated Text:", value=generated_text, height=200, max_chars=None, key=None)