77 lines
3.4 KiB
Python
77 lines
3.4 KiB
Python
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import csv
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import pandas as pd
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import os
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from openai import OpenAI
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import sys
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def classify_lines(input_file_path, audit_ai_csv) -> list[str]:
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from scriptAudit.utils import remove_empty_content,remove_leading_numbers, remove_numeric_only_content,remove_emptyline_rows,merge_consecutive_action_lines,merge_consecutive_dialogue_lines
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from scriptAudit.utils import insert_blank_lines,add_fade_in_out,remove_asterisks, merge_consecutive_action_lines_new,extract_labeled_lines, remove_trailing_speaker
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with open(input_file_path, "r") as f:
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raw_lines = [line.strip() for line in f.readlines() if line.strip()]
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chunked_results = []
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client = OpenAI(api_key=os.getenv('openai_key'))
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prompt = (
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"You are a screenplay assistant. For each line below, classify it using one of these labels: "
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"slugline, speaker, dialogue, action, parenthetical, transition. Return each line followed by its label in curly braces.\n\n"
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)
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for i in range(0, len(raw_lines), 20):
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chunk = raw_lines[i:i + 20]
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final_prompt = prompt + "\n".join(chunk)
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response = client.responses.create(
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model="gpt-4o",
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input=[
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{
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"role": "developer",
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"content": """You are a screenplay auditor. For each line below, classify it using one of these labels:
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slugline, speaker, dialogue, action, parenthetical, transition, special_term, title. Return each line followed by its label in curly braces.\n\n
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**Examples:**
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INT. ROOM – NIGHT {slugline}
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KITCHEN – DAY {slugline}
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JOHN {speaker}
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(quietly) {parenthetical}
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JOHN (O.S.) {speaker}
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JOHN (angrily) {speaker}
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I knew you’d come. {dialogue}
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She turns away from the window. {action}
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FADE OUT. {transition}
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THE END {title}
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(V.O.) {special_term}
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John CONT'D {speaker}"""
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},
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{
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"role": "user",
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"content": "I need you to classify the lines below. Please provide the classification in the format: 'line {label}'\n\n" + "\n".join(chunk)
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}
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]
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)
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classified = response.output_text.splitlines()
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chunked_results.extend(classified)
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extracted = extract_labeled_lines(chunked_results)
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with open(audit_ai_csv, mode='w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(["content", "script_element"])
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writer.writerows(extracted)
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print("Classification completed.")
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df = pd.read_csv(audit_ai_csv)
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df = remove_empty_content(df)
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df = remove_asterisks(df)
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df = remove_leading_numbers(df)
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df = remove_numeric_only_content(df)
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df = remove_emptyline_rows(df)
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df = remove_trailing_speaker(df)
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# df = merge_consecutive_action_lines(df)
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df = merge_consecutive_action_lines_new(df)
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df = merge_consecutive_dialogue_lines(df)
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df = insert_blank_lines(df)
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df = add_fade_in_out(df)
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return df
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