collect_by_task.py

import openai
import json
from tqdm import tqdm
import webvtt

f = open("/home/shang/openai-apikey.txt")
#print(f.readlines()[0])
openai.api_key = f.readlines()[0]
f.close()

f = open("/shared/medhini/COIN/COIN_annts.json")
data = json.load(f)
coffee = []

with open("./COIN/demo_coffee.txt","w") as f:
    for id in data.keys():
        if data[id] == "MakeCoffee":
            f.write(id + ",")
            coffee.append(id)

coffee = coffee[:-2]

steps = []
prompt = "Write the steps of the task that the person is demonstrating, based on the noisy transcript.\nTranscript: |||1\nSteps:\n1."
for i in tqdm(range(len(coffee))):
    try:
        f = open("/shared/medhini/COIN/coin_asr/" + coffee[i] + ".txt")
        transcript = " ".join(f.readlines())
        print(transcript)
        input_text = prompt.replace("|||1", transcript)
        response = openai.Completion.create(
                                engine="text-babbage-001",
                                prompt=input_text,
                                temperature=0.7,
                                max_tokens=256,
                                top_p=1,
                                frequency_penalty=0,
                                presence_penalty=0
                            )
        output = response["choices"][0]["text"].strip()
        steps.append(output)
    except:
        print(coffee[i])
        pass

prompt2 = "You take on the role of a professional summarizer. You are given a list of different methods to make coffee. For each method, you are given a list of steps. Use the given steps to construct a generalized recipe for making coffee. Do not rely on one method too much - generalize across all different methods.\nSteps: |||1\nSteps:\n1."
input_text2 = prompt2.replace("|||1", "\nMethod: ".join(steps))
response = openai.Completion.create(
                            engine="text-babbage-001",
                            prompt=input_text2,
                            temperature=0.7,
                            max_tokens=256,
                            top_p=1,
                            frequency_penalty=0,
                            presence_penalty=0
                        )
output = response["choices"][0]["text"].strip()

with open("./COIN/coffee_vtt.json","w") as f:
    json.dump(output, f)