app.py

import streamlit as st
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the Starcoder2 model and tokenizer
model_name = "starcoder2"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def code_complete(prompt, max_length=256):
    """
    Generate code completion suggestions for the given prompt.

    Args:
        prompt (str): The incomplete code snippet.
        max_length (int, optional): The maximum length of the generated code. Defaults to 256.

    Returns:
        list: A list of code completion suggestions.
    """
    # Tokenize the input prompt
    inputs = tokenizer.encode_plus(prompt, 
                                    add_special_tokens=True, 
                                    max_length=max_length, 
                                    padding="max_length", 
                                    truncation=True, 
                                    return_attention_mask=True, 
                                    return_tensors="pt")

    # Generate code completion suggestions
    outputs = model.generate(inputs["input_ids"], 
                             attention_mask=inputs["attention_mask"], 
                             max_length=max_length)

    # Decode the generated code
    suggestions = []
    for output in outputs:
        decoded_code = tokenizer.decode(output, skip_special_tokens=True)
        suggestions.append(decoded_code)

    return suggestions

def code_fix(code):
    """
    Fix errors in the given code snippet.

    Args:
        code (str): The code snippet with errors.

    Returns:
        str: The corrected code snippet.
    """
    # Tokenize the input code
    inputs = tokenizer.encode_plus(code, 
                                    add_special_tokens=True, 
                                    max_length=512, 
                                    padding="max_length", 
                                    truncation=True, 
                                    return_attention_mask=True, 
                                    return_tensors="pt")

    # Generate corrected code
    outputs = model.generate(inputs["input_ids"], 
                             attention_mask=inputs["attention_mask"], 
                             max_length=512)

    # Decode the generated code
    corrected_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return corrected_code

def text_to_code(text, max_length=256):
    """
    Generate code from a natural language description.

    Args:
        text (str): The natural language description of the code.
        max_length (int, optional): The maximum length of the generated code. Defaults to 256.

    Returns:
        str: The generated code.
    """
    # Tokenize the input text
    inputs = tokenizer.encode_plus(text, 
                                    add_special_tokens=True, 
                                    max_length=max_length, 
                                    padding="max_length", 
                                    truncation=True, 
                                    return_attention_mask=True, 
                                    return_tensors="pt")

    # Generate code from the input text
    outputs = model.generate(inputs["input_ids"], 
                             attention_mask=inputs["attention_mask"], 
                             max_length=max_length)

    # Decode the generated code
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

    return generated_code

# Create a Streamlit app
st.title("Codebot")
st.write("Welcome to the Codebot! You can use this app to generate code completions, fix errors in your code, or generate code from a natural language description.")

# Create a tab for code completion
code_completion_tab = st.tab("Code Completion")

with code_completion_tab:
    st.write("Enter an incomplete code snippet:")
    prompt_input = st.text_input("Prompt:", value="")
    generate_button = st.button("Generate Completions")

    if generate_button:
        completions = code_complete(prompt_input)
        st.write("Code completions:")
        for i, completion in enumerate(completions):
            st.write(f"{i+1}. {completion}")

# Create a tab for code fixing
code_fixing_tab = st.tab("Code Fixing")

with code_fixing_tab:
    st.write("Enter a code snippet with errors:")
    code_input = st.text_area("Code:", height=300)
    fix_button = st.button("Fix Errors")

    if fix_button:
        corrected_code = code_fix(code_input)
        st.write("Corrected code:")
        st.code(corrected_code)

# Create a tab for text-to-code
text_to_code_tab = st.tab("Text-to-Code")

with text_to_code_tab:
    st.write("Enter a natural language description of the code:")
    text_input = st.text_input("Description:", value="")
    generate_button = st.button("Generate Code")

    if generate_button:
        generated_code = text_to_code(text_input)
        st.write("Generated code:")
        st.code(generated_code)

# Run the Streamlit app
if __name__ == "__main__":
    st.run()