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from dora import DoraStatus
import pylcs
import os
import pyarrow as pa
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
import re
import time
CHATGPT = False
MODEL_NAME_OR_PATH = "/home/peiji/deepseek-coder-6.7B-instruct-GPTQ/"
CODE_MODIFIER_TEMPLATE = """
### Instruction
Respond with one block of modified code only in ```python block. No explaination.
```python
{code}
```
{user_message}
### Response:
"""
MESSAGE_SENDER_TEMPLATE = """
### Instruction
You're a json expert. Format your response as a json with a topic and a data field in a ```json block. No explaination needed. No code needed.
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)
- line: Int[4]
The response should look like this:
```json
[
{{ "topic": "line", "data": [10, 10, 90, 10] }},
]
```
{user_message}
### Response:
"""
ASSISTANT_TEMPLATE = """
### Instruction
You're a helpuf assistant named dora.
Reply with a short message. No code needed.
User {user_message}
### Response:
"""
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME_OR_PATH,
device_map="auto",
trust_remote_code=True,
revision="main",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
def extract_python_code_blocks(text):
"""
Extracts Python code blocks from the given text that are enclosed in triple backticks with a python language identifier.
Parameters:
- text: A string that may contain one or more Python code blocks.
Returns:
- A list of strings, where each string is a block of Python code extracted from the text.
"""
pattern = r"```python\n(.*?)\n```"
matches = re.findall(pattern, text, re.DOTALL)
if len(matches) == 0:
pattern = r"```python\n(.*?)(?:\n```|$)"
matches = re.findall(pattern, text, re.DOTALL)
if len(matches) == 0:
return [text]
else:
matches = [remove_last_line(matches[0])]
return matches
def extract_json_code_blocks(text):
"""
Extracts json code blocks from the given text that are enclosed in triple backticks with a json language identifier.
Parameters:
- text: A string that may contain one or more json code blocks.
Returns:
- A list of strings, where each string is a block of json code extracted from the text.
"""
pattern = r"```json\n(.*?)\n```"
matches = re.findall(pattern, text, re.DOTALL)
if len(matches) == 0:
pattern = r"```json\n(.*?)(?:\n```|$)"
matches = re.findall(pattern, text, re.DOTALL)
if len(matches) == 0:
return [text]
return matches
def remove_last_line(python_code):
"""
Removes the last line from a given string of Python code.
Parameters:
- python_code: A string representing Python source code.
Returns:
- A string with the last line removed.
"""
lines = python_code.split("\n") # Split the string into lines
if lines: # Check if there are any lines to remove
lines.pop() # Remove the last line
return "\n".join(lines) # Join the remaining lines back into a string
def calculate_similarity(source, target):
"""
Calculate a similarity score between the source and target strings.
This uses the edit distance relative to the length of the strings.
"""
edit_distance = pylcs.edit_distance(source, target)
max_length = max(len(source), len(target))
# Normalize the score by the maximum possible edit distance (the length of the longer string)
similarity = 1 - (edit_distance / max_length)
return similarity
def find_best_match_location(source_code, target_block):
"""
Find the best match for the target_block within the source_code by searching line by line,
considering blocks of varying lengths.
"""
source_lines = source_code.split("\n")
target_lines = target_block.split("\n")
best_similarity = 0
best_start_index = 0
best_end_index = -1
# Iterate over the source lines to find the best matching range for all lines in target_block
for start_index in range(len(source_lines) - len(target_lines) + 1):
for end_index in range(start_index + len(target_lines), len(source_lines) + 1):
current_window = "\n".join(source_lines[start_index:end_index])
current_similarity = calculate_similarity(current_window, target_block)
if current_similarity > best_similarity:
best_similarity = current_similarity
best_start_index = start_index
best_end_index = end_index
# Convert line indices back to character indices for replacement
char_start_index = len("\n".join(source_lines[:best_start_index])) + (
1 if best_start_index > 0 else 0
)
char_end_index = len("\n".join(source_lines[:best_end_index]))
return char_start_index, char_end_index
def replace_code_in_source(source_code, replacement_block: str):
"""
Replace the best matching block in the source_code with the replacement_block, considering variable block lengths.
"""
replacement_block = extract_python_code_blocks(replacement_block)[0]
start_index, end_index = find_best_match_location(source_code, replacement_block)
if start_index != -1 and end_index != -1:
# Replace the best matching part with the replacement block
new_source = (
source_code[:start_index] + replacement_block + source_code[end_index:]
)
return new_source
else:
return source_code
class Operator:
def on_event(
self,
dora_event,
send_output,
) -> DoraStatus:
if dora_event["type"] == "INPUT" and dora_event["id"] == "code_modifier":
input = dora_event["value"][0].as_py()
with open(input["path"], "r", encoding="utf8") as f:
code = f.read()
user_message = input["user_message"]
start_llm = time.time()
if CHATGPT:
output = self.ask_chatgpt(
CODE_MODIFIER_TEMPLATE.format(code=code, user_message=user_message)
)
else:
output = self.ask_llm(
CODE_MODIFIER_TEMPLATE.format(code=code, user_message=user_message)
)
source_code = replace_code_in_source(code, output)
print("response time:", time.time() - start_llm, flush=True)
send_output(
"modified_file",
pa.array(
[
{
"raw": source_code,
"path": input["path"],
"response": output,
"prompt": input["user_message"],
}
]
),
dora_event["metadata"],
)
print("response: ", output, flush=True)
send_output(
"assistant_message",
pa.array([output]),
dora_event["metadata"],
)
elif dora_event["type"] == "INPUT" and dora_event["id"] == "message_sender":
user_message = dora_event["value"][0].as_py()
output = self.ask_llm(
MESSAGE_SENDER_TEMPLATE.format(user_message=user_message)
)
outputs = extract_json_code_blocks(output)[0]
print("response: ", output, flush=True)
try:
outputs = json.loads(outputs)
if not isinstance(outputs, list):
outputs = [outputs]
for output in outputs:
if not isinstance(output["data"], list):
output["data"] = [output["data"]]
if output["topic"] in ["led", "blaster"]:
send_output(
output["topic"],
pa.array(output["data"]),
dora_event["metadata"],
)
send_output(
"assistant_message",
pa.array([f"sent: {output}"]),
dora_event["metadata"],
)
else:
send_output(
"assistant_message",
pa.array(
[f"Could not send as topic was not available: {output}"]
),
dora_event["metadata"],
)
except:
send_output(
"assistant_message",
pa.array([f"Could not parse json: {outputs}"]),
dora_event["metadata"],
)
# if data is not iterable, put data in a list
elif dora_event["type"] == "INPUT" and dora_event["id"] == "assistant":
user_message = dora_event["value"][0].as_py()
output = self.ask_llm(ASSISTANT_TEMPLATE.format(user_message=user_message))
send_output(
"assistant_message",
pa.array([output]),
dora_event["metadata"],
)
return DoraStatus.CONTINUE
def ask_llm(self, prompt):
# Generate output
# prompt = PROMPT_TEMPLATE.format(system_message=system_message, prompt=prompt))
input = tokenizer(prompt, return_tensors="pt")
input_ids = input.input_ids.cuda()
# add attention mask here
attention_mask = input["attention_mask"]
output = model.generate(
inputs=input_ids,
temperature=0.7,
do_sample=True,
top_p=0.95,
top_k=40,
max_new_tokens=512,
attention_mask=attention_mask,
eos_token_id=tokenizer.eos_token_id,
)
# Get the tokens from the output, decode them, print them
# Get text between im_start and im_end
return tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt) :]
def ask_chatgpt(self, prompt):
from openai import OpenAI
client = OpenAI()
print("---asking chatgpt: ", prompt, flush=True)
response = client.chat.completions.create(
model="gpt-4-turbo-preview",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
],
)
answer = response.choices[0].message.content
print("Done", flush=True)
return answer
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": "code_modifier",
"value": pa.array(
[
{
"path": path,
"user_message": "change planning to make gimbal follow bounding box ",
},
]
),
"metadata": [],
},
print,
)
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