app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer

# model_name = "flax-community/gpt-code-clippy-1.3B-apps-alldata"
model_name = "flax-community/gpt-code-clippy-125M-apps-alldata"

@st.cache(allow_output_mutation=True)
def get_model():
    return AutoModelForCausalLM.from_pretrained(model_name)

def get_tokenizer():
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    tokenizer.pad_token = tokenizer.eos_token
    return tokenizer

def format_input(question, starter_code=""):
    answer_type = "\nUse Call-Based format\n" if starter_code else \
                  "\nUse Standard Input format\n"
    return f"\nQUESTION:\n{question}\n{starter_code}\n{answer_type}\nANSWER:\n"


def generate_solution(model, tokenizer, question, starter_code="", temperature=1.0, num_beams=1):
    prompt = format_input(question, starter_code)
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    start = len(input_ids[0])
    
    output = model.generate(
        input_ids,
        max_length=start + 150,
        do_sample=True,
        top_p=0.95,
        pad_token_id=tokenizer.pad_token_id,
        early_stopping=True,
        temperature=temperature,
        num_beams=int(num_beams),
        no_repeat_ngram_size=None,
        repetition_penalty=None,
        num_return_sequences=None,
    )

    return tokenizer.decode(output[0][start:], skip_special_tokens=True).strip()


_EXAMPLES = [
    [
        """
Given a 2D list of size `m * n`. Your task is to find the sum of minimum value in each row.
For Example:
```python
[
  [1, 2, 3, 4, 5],       # minimum value of row is 1
  [5, 6, 7, 8, 9],       # minimum value of row is 5
  [20, 21, 34, 56, 100]  # minimum value of row is 20
]
```
So, the function should return `26` because sum of minimums is as `1 + 5 + 20 = 26`
        """,
        "",
        0.8,
    ],
    [
        """
# Personalized greeting

Create a function that gives a personalized greeting. This function takes two parameters: `name` and `owner`.
        """,
        """
Use conditionals to return the proper message:

case| return
--- | ---
name equals owner | 'Hello boss'
otherwise         | 'Hello guest'
def greet(name, owner):
        """,
        0.8,
    ],
]
def run():
    st.set_page_config(
        page_title="Code Clippy Problem Solver"
    )
    # sidebar
    st.sidebar.title("Code Clippy")
    st.sidebar.image(
        "https://raw.githubusercontent.com/ncoop57/gpt-code-clippy/camera-ready/code_clippy_logo.jpg",
        caption="(c) awesome Aimee Trevett",
    )
    st.sidebar.markdown("[Github](https://github.com/ncoop57/gpt-code-clippy)")
    
    st.sidebar.markdown("### Controls:")
    
    temperature = st.sidebar.slider(
        "Temperature",
        min_value=0.5,
        max_value=1.5,
        value=0.8,
        step=0.1,
    )
    num_beams = st.sidebar.slider(
        "Num beams",
        min_value=1,
        max_value=4,
        step=1,
    )

    # main body
    model = get_model()
    tokenizer = get_tokenizer()

    question = st.text_input(
        "Problem: ",
        value="A function that can greet user by name. Given a name it should say hello to user.",
        help="Text description of the coding problem to be solved",
    )
    starter_code = st.text_input(
        "Started code: ",
        value="def greet(name):",
        help="Optional starter code"
    )
    submit_button = st.button("Solve")

    if submit_button:
        
        generate_solution(model, tokenizer, question, starter_code, temperature, num_beams)
        st.code(tmp, language="python")
    

if __name__=="__main__":
    run()