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from dora import DoraStatus
import pylcs
import textwrap
import pandas as pd
import os
import pyarrow as pa
import numpy as np
from ctransformers import AutoModelForCausalLM
import json
MIN_NUMBER_LINES = 4
MAX_NUMBER_LINES = 21
def search_most_simlar_line(text, searched_line):
lines = text.split("\n")
values = []
for line in lines[MIN_NUMBER_LINES:MAX_NUMBER_LINES]:
values.append(pylcs.edit_distance(line, searched_line))
output = lines[np.array(values).argmin() + MIN_NUMBER_LINES]
return output
def strip_indentation(code_block):
# Use textwrap.dedent to strip common leading whitespace
dedented_code = textwrap.dedent(code_block)
return dedented_code
def replace_code_with_indentation(original_code, replacement_code):
# Split the original code into lines
lines = original_code.splitlines()
if len(lines) != 0:
# Preserve the indentation of the first line
indentation = lines[0][: len(lines[0]) - len(lines[0].lstrip())]
# Create a new list of lines with the replacement code and preserved indentation
new_code_lines = indentation + replacement_code
else:
new_code_lines = replacement_code
return new_code_lines
def replace_source_code(source_code, gen_replacement):
initial = search_most_simlar_line(source_code, gen_replacement)
print("Initial source code: %s" % initial)
replacement = strip_indentation(
gen_replacement.replace("```python\n", "")
.replace("\n```", "")
.replace("\n", "")
)
intermediate_result = replace_code_with_indentation(initial, replacement)
print("Intermediate result: %s" % intermediate_result)
end_result = source_code.replace(initial, intermediate_result)
return end_result
def save_as(content, path):
# use at the end of replace_2 as save_as(end_result, "file_path")
with open(path, "w") as file:
file.write(content)
class Operator:
def __init__(self):
# Load tokenizer
self.llm = AutoModelForCausalLM.from_pretrained(
"TheBloke/OpenHermes-2.5-Mistral-7B-GGUF",
model_file="openhermes-2.5-mistral-7b.Q4_K_M.gguf",
model_type="mistral",
gpu_layers=50,
)
def on_event(
self,
dora_event,
send_output,
) -> DoraStatus:
if dora_event["type"] == "INPUT":
input = dora_event["value"][0].as_py()
if False:
with open(input["path"], "r", encoding="utf8") as f:
raw = f.read()
prompt = f"{raw[:400]} \n\n {input['query']}. "
output = self.ask_mistral(
"You're a python code expert. Respond with only one line of code that modify a constant variable. Keep the uppercase.",
prompt,
)
print("output: {}".format(output))
source_code = replace_source_code(raw, output)
send_output(
"output_file",
pa.array(
[
{
"raw": source_code,
"path": input["path"],
"response": output,
"prompt": prompt,
}
]
),
dora_event["metadata"],
)
else:
print("input: ", input, flush=True)
output = self.ask_mistral(
"""You're a json expert. Format your response as a json with a topic field and a data field.
The schema for those json are:
- led: Int[3] (min: 0, max: 255)
- blaster: Int (min: 0, max: 128)
- control: Int[3] (min: -1, max: 1)
- rotation: Int[2] (min: -55, max: 55)
""",
input["query"],
)
print("output: {}".format(output), flush=True)
try:
output = json.loads(output)
if not isinstance(output["data"], list):
output["data"] = [output["data"]]
if output["topic"] in ["led", "blaster", "control", "rotation"]:
print("output", output)
send_output(
output["topic"],
pa.array(output["data"]),
dora_event["metadata"],
)
except:
print("Could not parse json")
# if data is not iterable, put data in a list
return DoraStatus.CONTINUE
def ask_mistral(self, system_message, prompt):
prompt_template = f"""<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
# Generate output
outputs = self.llm(
prompt_template,
)
# Get the tokens from the output, decode them, print them
# Get text between im_start and im_end
return outputs.split("<|im_end|>")[0]
if __name__ == "__main__":
op = Operator()
# Path to the current file
current_file_path = __file__
# Directory of the current file
current_directory = os.path.dirname(current_file_path)
path = current_directory + "/planning_op.py"
with open(path, "r", encoding="utf8") as f:
raw = f.read()
op.on_event(
{
"type": "INPUT",
"id": "tick",
"value": pa.array(
[
{
"raw": raw,
"path": path,
"query": "le control a 1 0 0",
}
]
),
"metadata": [],
},
print,
)
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