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import parser
import requests
import re
def zephyr_score(sentence):
prompt = f"""<|user|>
You are an assistant helping with paper reviews.
You will be tasked to classify sentences as 'J' or 'V'
'J' is positive or 'J' is encouraging.
'J' has a neutral tone or 'J' is professional.
'V' is overly blunt or 'V' contains excessive negativity and no constructive feedback.
'V' contains an accusatory tone or 'V' contains sweeping generalizations or 'V' contains personal attacks.
Text: "{sentence}"
Please classify this text as either 'J' or 'V'. Only output 'J' or 'V' with no additional explanation.<|endoftext|>
<|assistant|>
"""
return prompt
def zephyr_revise(sentence):
prompt = f"""<|user|>
You are an assistant that helps users revise Paper Reviews.
Paper reviews exist to provide authors of academic research papers constructive critism.
This is text found in a review.
This text was classified as 'toxic':
Text: "{sentence}"
Please revise this text such that it maintains the criticism in the original text and delivers it in a friendly but professional manner. Make minimal changes to the original text.<|endoftext|>
<|assistant|>
"""
return prompt
def mistral_score(sentence):
prompt = f"""<s>[INST]
You are an assistant helping with paper reviews.
You will be tasked to classify sentences as 'J' or 'V'
'J' is positive or 'J' is encouraging.
'J' has a neutral tone or 'J' is professional.
'V' is overly blunt or 'V' contains excessive negativity and no constructive feedback.
'V' contains an accusatory tone or 'V' contains sweeping generalizations or 'V' contains personal attacks.
Text: "{sentence}"
Please classify this text as either 'J' or 'V'. Only output 'J' or 'V' with no additional explanation. [/INST]"""
return prompt
def mistral_revise(sentence):
prompt = f"""<s>[INST]
You are an assistant that helps users revise Paper Reviews.
Paper reviews exist to provide authors of academic research papers constructive critism.
This is text found in a review.
This text was classified as 'toxic':
Text: "{sentence}"
Please revise this text such that it maintains the criticism in the original text and delivers it in a friendly but professional manner. Make minimal changes to the original text. [/INST] Revised Text: """
return prompt
def phi_score(sentence):
prompt = f"""<|system|>
You are an assistant helping with paper reviews.
You will be tasked to classify sentences as 'J' or 'V'
'J' is positive or 'J' is encouraging.
'J' has a neutral tone or 'J' is not discouraging and not encouraging.
'V' is overly blunt or 'V' contains excessive negativity and no constructive feedback.
'V' contains an accusatory tone or 'V' contains sweeping generalizations or 'V' contains personal attacks.<|end|>
<|user|>
Text: "{sentence}"
Please classify this text as either 'J' or 'V'. Only output 'J' or 'V' with no additional explanation.<|end|>
<|assistant|>
"""
return prompt
def phi_revise(sentence):
prompt = f"""<|system|>
You are an assistant that helps users revise Paper Reviews.
Paper reviews exist to provide authors of academic research papers constructive critism.<|end|>
<|user|>
This is text found in a review.
This text was classified as 'toxic'
Text: "{sentence}"
Please revise this text such that it maintains the criticism in the original text and delivers it in a friendly but professional manner. Make minimal changes to the original text.<|end|>
<|assistant|>
"""
return prompt
def query_model_score(sentence, api_key, model_id, prompt_fun):
API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
headers = {"Authorization": f"Bearer {api_key}"}
prompt = prompt_fun(sentence)
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
parameters = {"max_new_tokens" : 5, "temperature": 0.1, "return_full_text": False}
options = {"wait_for_model": True}
data = query({"inputs": f"{prompt}", "parameters": parameters, "options": options})
score = data[0]['generated_text']
if 'v' in score.lower():
return 1
else:
return 0
def query_model_revise(sentence, api_key, model_id, prompt_fun):
API_URL = f"https://api-inference.huggingface.co/models/{model_id}"
headers = {"Authorization": f"Bearer {api_key}"}
prompt = prompt_fun(sentence)
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
parameters = {"max_new_tokens" : 200, "temperature": 0.1, "return_full_text": False}
options = {"wait_for_model": True}
data = query({"inputs": f"{prompt}", "parameters": parameters, "options": options})
revision = data[0]['generated_text']
return revision
def extract_quotes(text):
pattern = r'"([^"]*)"'
matches = re.findall(pattern, text)
if matches:
return ' '.join(matches)
else:
return text
def revise_review(review, api_key, model_id, highlight_color):
result = {
"success": False,
"data": {
"revision": "",
"score": "",
"sentence_count": "",
"revised_sentences": ""
},
"message": ""
}
if 'zephyr' in model_id.lower():
revision_prompt = zephyr_revise
score_prompt = zephyr_score
elif 'mistral' in model_id.lower():
revision_prompt = mistral_revise
score_prompt = mistral_score
elif 'phi' in model_id.lower():
revision_prompt = phi_revise
score_prompt = phi_score
try:
review = review.replace('"', "'")
sentences = parser.parse_sentences(review)
review_score = 0
revision_count = 0
review_revision = ""
for sentence in sentences:
if len(sentence) > 10:
score = query_model_score(sentence, api_key, model_id, score_prompt)
if score == 0:
review_revision += " " + sentence
else:
review_score = 1
revision_count +=1
revision = query_model_revise(sentence, api_key, model_id, revision_prompt)
revision = extract_quotes(revision)
revision = revision.strip().strip('"')
review_revision += f" <div style='background-color: {highlight_color}; display: inline;'>{revision}</div>"
else:
review_revision += " " + sentence
# end revision/prepare return json
result["success"] = True
result["message"] = "Review successfully revised!"
result["data"]["revision"] = review_revision
result["data"]["score"] = review_score
result["data"]["sentence_count"] = sum(1 for sentence in sentences if len(sentence) > 20)
result["data"]["revised_sentences"] = revision_count
except Exception as e:
result["message"] = str(e)
return result
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