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| import urllib | |
| import streamlit as st | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # model_name = "flax-community/gpt-neo-1.3B-apps-all" | |
| model_name = "flax-community/gpt-neo-125M-apps-all" | |
| def get_model(): | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| return (model, 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 clean_text(generation): | |
| # clean up text has discussed in OpenAI's paper "Evaluating Large Language Models Trained on Code" | |
| generation = generation.split("\ndef")[0] | |
| generation = generation.split("\nclass")[0] | |
| generation = generation.split("\n#")[0] | |
| generation = generation.split("\nif")[0] | |
| return generation | |
| 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, | |
| eos_token_id=tokenizer.eos_token_id, | |
| early_stopping=True, | |
| temperature=temperature, | |
| num_beams=int(num_beams), | |
| no_repeat_ngram_size=None, | |
| repetition_penalty=None, | |
| num_return_sequences=None, | |
| ) | |
| output_str = tokenizer.decode(output[0][start:], skip_special_tokens=True).strip() | |
| output_str = clean_text(output_str) | |
| return output_str | |
| _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("[Report](https://github.com/ncoop57/gpt-code-clippy/wiki)") | |
| 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, tokenizer = get_model() | |
| 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: | |
| text = st.text("Generating solution...") | |
| # gif from https://giphy.com/gifs/alan-DfSXiR60W9MVq | |
| gif_runner = st.image("./loading.gif") | |
| output = generate_solution( | |
| model, tokenizer, question, starter_code, temperature, num_beams | |
| ) | |
| text.empty() | |
| gif_runner.empty() | |
| st.text("Solution:") | |
| st.code(output, language="python") | |
| # Create link to carbon to make a nice screenshot of the generated code | |
| url_code = urllib.parse.quote(f"# {question}\n{output}") | |
| st.markdown( | |
| f"[Would you like a Carbon Copy?](https://carbon.now.sh/?bg=rgba%280%2C0%2C0%2C0%29&t=seti&wt=none&l=python&ds=false&dsyoff=20px&dsblur=68px&wc=true&wa=false&pv=56px&ph=56px&ln=false&fl=1&fm=Hack&fs=14px&lh=133%25&si=false&es=2x&wm=false&code={url_code})" | |
| ) | |
| if __name__ == "__main__": | |
| run() |