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| from bert_similarity import get_similarity | |
| from text_generator import get_gpt_response | |
| from arte_score import ping_api | |
| def generate_user_prompt(prompt_type, base_text): | |
| prompts = { | |
| "too_simple": f""" | |
| Convert this text to a higher reading level of the original text. | |
| The higher reading level text should have more syllables per word and more words per sentence. | |
| It should retain the core meaning of the original text. | |
| The output text should also have a similar number of total words as the input text. | |
| Here is the input text: | |
| {base_text} | |
| """, | |
| "too_complex": f""" | |
| Convert this text to a simpler version of the original text. | |
| The simpler version should have simpler words, fewer syllables per word, and fewer words per sentence. | |
| It should retain the core meaning of the original text. | |
| The output text should also have a similar number of total words as the input text. | |
| Here is the input text: | |
| {base_text} | |
| """ | |
| } | |
| return prompts[prompt_type].format(base_text=base_text) | |
| __model_types = ["FRE", "SBERT"] | |
| fre_levels = { | |
| "5th Grade (90-100)": (90, 100), | |
| "6th Grade (80-90)": (80, 90), | |
| "7th Grade (70-80)": (70, 80), | |
| "8th - 9th Grade (60-70)": (60, 70), | |
| "10th - 12th Grade (50-60)": (50, 60), | |
| "College (30-50)": (30, 50), | |
| "College Graduate + Professionals (0-30)": (0, 30), | |
| "Research + Nobel laureate ((-infinity)-(-1))": (-float('inf'), -1) | |
| } | |
| sbert_levels = { | |
| "Easy (> -0.04)": (-0.04, 100), | |
| "Somewhat Easy (-0.64 to -0.05)": (-0.64, -0.05), | |
| "Intermediate (-1.20 to -0.65)": (-1.20, -0.65), | |
| "Somewhat Difficult (-1.87 to -1.21)": (-1.87, -1.21), | |
| "Difficult (-100 to -1.88)": (-100, -1.88) | |
| } | |
| # Setting FRE to be default. | |
| __reading_levels = fre_levels | |
| __inverse_reading_levels = {v: k for k, v in __reading_levels.items()} | |
| def __set_reading_levels(level_type): | |
| global __reading_levels | |
| global __inverse_reading_levels | |
| if level_type == "FRE": | |
| __reading_levels = fre_levels | |
| elif level_type == "SBERT": | |
| __reading_levels = sbert_levels | |
| __inverse_reading_levels = {v: k for k, v in __reading_levels.items()} | |
| return level_type | |
| def fetch_readability_score_fre(input_text): | |
| __set_reading_levels(__model_types[0]) | |
| print(f"=============== Setting Reading Levels to FRE ===============") | |
| print(f"Reading Level Dictionary: {__reading_levels}\n") | |
| print(f"Reading Level Dictionary: {__inverse_reading_levels}") | |
| print(f"=============== Finished Setting Reading Levels to FRE ===============") | |
| return __user_input_readability_level(input_text, True) | |
| def fetch_readability_score_sbert(input_text): | |
| __set_reading_levels(__model_types[1]) | |
| print(f"=============== Setting Reading Levels to SBERT ===============") | |
| print(f"Reading Level Dictionary: {__reading_levels}\n") | |
| print(f"Reading Level Dictionary: {__inverse_reading_levels}") | |
| print(f"=============== Finished Setting Reading Levels to SBERT ===============") | |
| return __user_input_readability_level(input_text, False) | |
| def __user_input_readability_level(input_text, is_fre): | |
| current_score = ping_api(input_text, __model_types[0] if is_fre else __model_types[1]) | |
| print(f'Reading score for user input is: {current_score} for model type: {__model_types[0] if is_fre else __model_types[1]}') | |
| current_level = '' | |
| for (min, max), level in __inverse_reading_levels.items(): | |
| if min <= current_score <= max: | |
| print(f'Reading level for user input is: {level}') | |
| current_level = level | |
| break | |
| return current_score, current_level | |
| def generate_sim_fre(input_text, min_reading_level, max_reading_level, min_entailment, system_prompt, max_iter, curr_reading_level): | |
| return __generate_similar_sentence(input_text, min_reading_level, max_reading_level, min_entailment, system_prompt, max_iter, curr_reading_level, True) | |
| def generate_sim_sbert(input_text, min_reading_level, max_reading_level, min_entailment, system_prompt, max_iter, curr_reading_level): | |
| return __generate_similar_sentence(input_text, min_reading_level, max_reading_level, min_entailment, system_prompt, max_iter, curr_reading_level, False) | |
| def __generate_similar_sentence(input_text, min_reading_level, max_reading_level, min_entailment, system_prompt, max_iter, curr_reading_level, is_fre): | |
| i = 0 | |
| completed = False | |
| user_prompt = "" | |
| curr_text = input_text | |
| similarity = 0 | |
| reading_level = 0 | |
| generated_texts = [] | |
| generated_text_scores = [] | |
| result_index = -1 | |
| closeness = float('inf') | |
| if min_reading_level < curr_reading_level < max_reading_level: | |
| return input_text, 1, curr_reading_level, "Input text was already within the target reading level!" | |
| else: | |
| while i < max_iter and not completed: | |
| if curr_reading_level > max_reading_level: | |
| print(f"Too simple, current reading level is {curr_reading_level}") | |
| user_prompt = generate_user_prompt("too_simple", curr_text) | |
| elif curr_reading_level < min_reading_level: | |
| print(f"Too complex, current reading level is {curr_reading_level}") | |
| user_prompt = generate_user_prompt("too_complex", curr_text) | |
| elif similarity < min_entailment: | |
| print(f"Entailment level is too low: {similarity}") | |
| user_prompt = f"Can you convert this text '{input_text}' to a grade level more similar to this text '{curr_text}'" | |
| else: | |
| print(f"Reading level is within target range: {curr_reading_level}") | |
| completed = True | |
| break | |
| response = get_gpt_response(user_prompt, system_prompt) | |
| # We add the generated text to the list of generated texts. | |
| generated_texts.append(response) | |
| similarity = get_similarity(response, input_text) | |
| reading_level = ping_api(response, __model_types[0] if is_fre else __model_types[1]) | |
| # We add the generated text's reading level to the list. | |
| generated_text_scores.append((similarity, reading_level)) | |
| # Determine the closeness of the reading level to the target reading level and store. | |
| if similarity >= min_entailment and min_reading_level <= reading_level <= max_reading_level: | |
| result_index = i | |
| completed = True | |
| break | |
| elif result_index == -1: | |
| result_index = i | |
| elif ((reading_level < min_reading_level and abs(reading_level - min_reading_level) < closeness) \ | |
| or (reading_level > max_reading_level and abs(reading_level - max_reading_level) < closeness)) and \ | |
| (similarity > generated_text_scores[result_index][0] or similarity > min_entailment): | |
| closeness = abs(reading_level - min_reading_level) if reading_level < min_reading_level else abs(reading_level - max_reading_level) | |
| result_index = i | |
| curr_text = response | |
| curr_reading_level = reading_level | |
| i += 1 | |
| # Printing all generated texts to the console. | |
| print(f"=============== Model Type: {__model_types[0] if is_fre else __model_types[1]} ===============\n") | |
| for index, text in enumerate(generated_texts): | |
| print(f"=============== Iteration {index} ===============") | |
| print(f"Generated text: {text}\nSimilarity: {generated_text_scores[index][0]}\nReadability Score: {generated_text_scores[index][1]}\n") | |
| # Printing the selected index and text. | |
| print(f"=============== Start Printing Selected Information ===============") | |
| print(f"Selected Iteration: {result_index + 1}") | |
| print(f"Selected Result: {generated_texts[result_index]}") | |
| print(f"Selected Result's Similarity score: {generated_text_scores[result_index][0]}") | |
| print(f"Selected Result's Readability score: {generated_text_scores[result_index][1]}") | |
| print(f"=============== End Printing Selected Information ===============") | |
| # Returning the final result. | |
| if completed: | |
| return generated_texts[result_index], generated_text_scores[result_index][0], generated_text_scores[result_index][1], "Success! Please see the converted text at your target reading level." | |
| else: | |
| return generated_texts[result_index], generated_text_scores[result_index][0], generated_text_scores[result_index][1], "Failed. We could not reach the target reading level while maintaining the text meaning." |