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import base64
import io
import json
import os
import re
from functools import cache
from litellm import completion
from pydantic import BaseModel
try:
from dotenv import load_dotenv
load_dotenv()
except:
pass
generation_config = {
"temperature": 0.9, # Temperature of the sampling distribution
"top_p": 1, # Probability of sampling from the top p tokens
"top_k": 1, # Number of top tokens to sample from
"max_output_tokens": 2048,
}
class TextEdits(BaseModel):
term: str
start_char: int
end_char: int
type: str
fix: str
reason: str
class SuggestedEdits(BaseModel):
edits: list[TextEdits]
safety_settings = [
{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_NONE"},
{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_ONLY_HIGH"},
]
gemini_models = [
{
"name": "Gemini 2.0 Flash",
"model": "gemini/gemini-2.0-flash",
"image_support": True,
},
{
"name": "Gemini 1.5 Pro",
"model": "gemini/gemini-1.5-pro",
"image_support": False,
},
]
models_dict = {model["name"]: model for model in gemini_models}
@cache
def get_file(relative_path: str) -> str:
current_path = os.path.dirname(os.path.abspath(__file__))
full_path = os.path.join(current_path, relative_path)
with open(full_path) as f:
return f.read()
def html_title(title: str) -> str:
return f"<h1>{title}</h1>"
def apply_review(text: str, review: list[dict]) -> str:
output = ""
review = sorted(review, key=lambda x: x["start_char"])
last_end = 0
for entity in review:
starts = [
m.start() + last_end
for m in re.finditer(entity["term"].lower(), text[last_end:].lower())
]
if len(starts) > 0:
start = starts[0]
end = start + len(entity["term"])
output += text[last_end:start]
if "fix" not in entity:
entity["fix"] = ""
if len(entity["fix"]) > 0:
output += get_file("templates/correction.html").format(
term=text[start:end], fix=entity["fix"], kind=entity["type"]
)
else:
output += get_file("templates/deletion.html").format(
term=text[start:end], kind=entity["type"]
)
last_end = end
output += text[last_end:]
return f"<pre style='white-space: pre-wrap;'>{output}</pre>"
def review_table_summary(review: list[dict]) -> str:
table = "<table><tr><th>Term</th><th>Fix</th><th>Type</th><th>Reason</th></tr>"
for entity in review:
table += f"<tr><td>{entity['term']}</td><td>{entity['fix']}</td><td>{entity['type']}</td><td>{entity.get('reason', '-')}</td></tr>"
table += "</table>"
return table
def review_text(model: str, text: str) -> list[dict]:
template = get_file("templates/prompt_v1.txt")
try:
response = completion(
model=model,
messages=[{"role": "user", "content": template.format(text=text)}],
response_format=SuggestedEdits,
)
except Exception as e:
print(e)
raise ValueError(
f"Error while getting answer from the model, make sure the content isn't offensive or dangerous."
)
return json.loads(response.choices[0].message.content)["edits"]
def process_text(model: str, text: str) -> str:
review = review_text(models_dict[model]["model"], text)
if len(review) == 0:
return html_title("No issues found in the text 🎉🎉🎉")
return (
html_title("Reviewed text")
+ apply_review(text, review)
+ html_title("Explanation")
+ review_table_summary(review)
)
def image_to_base64_string(img):
buffered = io.BytesIO()
img.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def process_image(model: str, image) -> list[dict]:
prompt = get_file("templates/prompt_image_v1.txt")
try:
response = completion(
model=models_dict[model]["model"],
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": "data:image/jpeg;base64," + image_to_base64_string(image),
},
],
}
],
)
except ValueError as e:
print(e)
message = f"Error while getting answer from the model, make sure the content isn't offensive or dangerous. Please try again or change the prompt. {str(e)}"
raise ValueError(message)
return response.choices[0].message.content
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