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import json
import os
import re
from functools import cache
import google.generativeai as genai
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,
}
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_1_0 = genai.GenerativeModel(
model_name="gemini-1.0-pro",
generation_config=generation_config,
safety_settings=safety_settings,
)
gemini_1_5 = genai.GenerativeModel(
model_name="gemini-1.5-pro-latest",
generation_config=generation_config,
safety_settings=safety_settings,
)
gemini_1_0_vision = genai.GenerativeModel(
"gemini-pro-vision",
generation_config=generation_config,
safety_settings=safety_settings,
)
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
@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 fix_json(json_str: str) -> str:
template = get_file("templates/prompt_json_fix.txt")
prompt = template.format(json=json_str)
response = gemini_1_0.generate_content(prompt).text
return response.split("```json")[1].split("```")[0]
def get_json_content(response: str) -> dict:
if "```json" not in response:
return []
raw_json = response.split("```json")[1].split("```")[0]
try:
return json.loads(raw_json)
except json.JSONDecodeError as e:
print(e)
new_json = fix_json(raw_json)
print(new_json)
return json.loads(new_json)
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 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(text: str, text_model: genai.GenerativeModel) -> list[dict]:
template = get_file("templates/prompt_v1.txt")
try:
response = text_model.generate_content(template.format(text=text)).text
except ValueError as e:
print(e)
raise ValueError(
f"Error while getting answer from the model, make sure the content isn't offensive or dangerous."
)
return get_json_content(response)
def process_text(model: str, text: str) -> str:
text_model = gemini_1_0 if model == "Gemini 1.0 Pro" else gemini_1_5
review = review_text(text, text_model)
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 review_image(image, vision_model: genai.GenerativeModel) -> list[dict]:
prompt = get_file("templates/prompt_image_v1.txt")
try:
response = vision_model.generate_content([prompt, image]).text
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
def process_image(model: str, image):
vision_model = gemini_1_0_vision if model == "Gemini 1.0 Pro Vision" else gemini_1_5
return review_image(image, vision_model)
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