add app and requirements

.vscode/settings.json

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+{}

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import pipeline
+
+@st.cache(allow_output_mutation=True)
+def load_tokenizer(model_ckpt):
+    return AutoTokenizer.from_pretrained(model_ckpt)
+
+@st.cache(allow_output_mutation=True)
+def load_model(model_ckpt):
+    model = AutoModelForCausalLM.from_pretrained(model_ckpt)
+    return model
+
+st.set_page_config(page_icon=':parrot:', layout="wide")
+
+default_code = '''\
+def print_hello_world():\
+'''
+
+model_ckpt = "models/codeparrot-small"
+tokenizer = load_tokenizer(model_ckpt)
+model = load_model(model_ckpt)
+gen_kwargs = {}
+
+st.title("CodeParrot 🦜")
+st.markdown('##')
+
+pipe = pipeline('text-generation', model=model, tokenizer=tokenizer)
+st.sidebar.header("Generation settings:")
+gen_kwargs["do_sample"] = st.sidebar.radio("Decoding strategy",  ["Greedy", "Sample"]) == "Sample"
+gen_kwargs["max_new_tokens"] = st.sidebar.slider("Number of tokens to generate", value=16, min_value = 8, max_value=256)
+if gen_kwargs["do_sample"]:
+    temperature = st.sidebar.slider("Temperature", value = 0.2, min_value = 0.0, max_value=2.0, step=0.05)
+    gen_kwargs["top_k"] = st.sidebar.slider("Top-k", min_value = 0, max_value=100, value = 0)
+    gen_kwargs["top_p"] = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.01, value = 0.95)
+
+gen_prompt = st.text_area("Generate code with prompt:", value=default_code, height=220,).strip()
+if st.button("Generate code!"):
+    generated_text = pipe(gen_prompt, **gen_kwargs)[0]['generated_text']
+    st.code(generated_text)

requirements.txt

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+transformers==4.12.2
+torch