import openaidef gpt3complete(speech):    # Completion function call engine: text-davinci-002    Platformresponse = openai.Completion.create(        engine="text-davinci-002",        prompt="Write a short summary of this text: {}".format(speech),        temperature=0.7,        max_tokens=1500,        top_p=1,        frequency_penalty=0,        presence_penalty=0,        )    return Platformresponse.choices[0].text

import openaiimport gpt3# GPT-3 API Keyopenai.api_key = "MY_API_KEY"

@app.route('/whisper', methods=['POST'])def handler():    if not request.files:        # If the user didn't submit any files, return a 400 (Bad Request) error.        abort(400)    # For each file, let's store the results in a list of dictionaries.    results = []    # Loop over every file that the user submitted.    for filename, handle in request.files.items():        # Create a temporary file.        # The location of the temporary file is available in `temp.name`.        temp = NamedTemporaryFile()        # Write the user's uploaded file to the temporary file.        # The file will get deleted when it drops out of scope.        handle.save(temp)        # Let's get the transcript of the temporary file.        result = model.transcribe(temp.name)        text = result['text']        # Let's get the summary of the soundfile        summary = gpt3.gpt3complete(text)        # Now we can store the result object for this file.        results.append({            'filename': filename,            'transcript': text.strip(),            'summary': summary.strip(),        })    # This will be automatically converted to JSON.    return {'results': results}

docker build -t whisper-api .

docker run -p 5000:5000 whisper-api

curl -F "file=@/path/to/file" http://localhost:5000/whisper