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import os |
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from dotenv import load_dotenv |
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import openai |
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import random |
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import requests |
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from ast import literal_eval |
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import json |
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from enum import Enum |
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import gradio as gr |
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load_dotenv() |
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openai.api_key = os.getenv("OPENAI_API_KEY") |
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CHAT_ENDPOINT="https://api.openai.com/v1/chat/completions" |
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CHAT_MODEL = "gpt-3.5-turbo" |
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CHAT_AUTH = {"Authorization": "Bearer " + openai.api_key} |
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MAX_TOKENS = 250 |
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class ChatRoles(): |
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SYSTEM = "system" |
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ASSISTANT = "assistant" |
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USER = "user" |
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def get_assistant_response(gpt_history): |
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params = { |
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"model": CHAT_MODEL, |
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"messages": gpt_history, |
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"max_tokens": MAX_TOKENS |
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} |
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response = requests.post(url=CHAT_ENDPOINT, json=params, headers=CHAT_AUTH) |
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print(literal_eval(response.content.decode("utf-8"))) |
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response_message = literal_eval(response.content.decode("utf-8"))["choices"][0]["message"]["content"] |
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gpt_history.append({"role": ChatRoles.ASSISTANT, "content": response_message}) |
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print("\n" + response_message) |
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return response_message |
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hardcoded = { |
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1: "Hi, I'm an AI powered college counselor from Cledge! What prompt do you want help with?", |
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2: "Pick as many questions to answer as you'd like. Write the number of the question and then your response." |
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} |
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instructions = { |
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2: "Based on these responses, generate 5 questions to help them brainstorm.", |
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3: "Based on these responses, ask follow up questions that help them narrow down the focus of the essay", |
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4: "Based on these responses, ask follow up questions that help them identify key themes in the essay", |
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5: "Based on these responses, think of 5 ideas for personal statement essays. Write a synopsis of each idea.", |
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} |
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def grad_demo(): |
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with gr.Blocks() as demo: |
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gpt_history = [] |
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def user(user_message, history): |
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gpt_history.append({"role": ChatRoles.USER, "content": user_message}) |
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print(f"Length of gpt_history: {gpt_history}") |
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return "", history + [[user_message, None]] |
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def bot(history): |
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step = len(history) |
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print(f"STEP: {step}") |
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bot_message = "" |
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if step in instructions: |
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gpt_history.append({"role": ChatRoles.SYSTEM, "content": instructions[step]}) |
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bot_message = get_assistant_response(gpt_history) |
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if step in hardcoded: |
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bot_message = f"{bot_message}\n\n {hardcoded[step]}" |
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history[-1][1] = bot_message |
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gpt_history.append({"role": ChatRoles.ASSISTANT, "content": bot_message}) |
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print(f"Length of gpt_history: {gpt_history}") |
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return history |
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def initialize(): |
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gpt_history.clear() |
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history = bot([[None, None]]) |
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return history |
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chatbot = gr.Chatbot(value = initialize) |
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msg = gr.Textbox() |
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clear = gr.Button("Clear") |
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msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( |
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bot, chatbot, chatbot |
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) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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demo.launch() |
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if __name__ == "__main__": |
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grad_demo() |
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