models.py

import parser
import requests

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', 'W', or 'V'. Only output 'J', 'W', 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 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):
        print(payload)
        response = requests.post(API_URL, headers=headers, json=payload)
        return response.json()
    parameters = {"max_new_tokens" : 20, "temperature": 0.0, "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": 0.0, "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 revise_review(review, api_key, model_id, highlight_color):
    result = {
        "success": False,
        "data": {
            "revision": "",
            "score": "",
            "sentence_count": "",
            "revised_sentences": ""
        },
        "message": ""
    }

    try:
        review = review.replace('"', "'")
        sentences = parser.parse_sentences(review)
        review_score = 0
        revision_count = 0
        review_revision = ""
        for sentence in sentences:
            if len(sentence) > 20:
                score = query_model_score(sentence, api_key, model_id, zephyr_score)
                if score == 0:
                    review_revision += " " + sentence
                else:
                    review_score = 1
                    revision_count +=1
                    revision = query_model_revise(sentence, api_key, model_id, zephyr_revise)
                    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