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import os | |
import subprocess | |
import time | |
def record_audio(): | |
audio_file_path = "audio_output.wav" | |
command = 'arecord -f cd -r 16000 ' + audio_file_path | |
subprocess.run(command, shell=True) |
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import openai | |
import os | |
openai.api_key = os.environ["OPENAI_API_KEY"] | |
def get_chatgpt_response(prompt_question): | |
response = openai.ChatCompletion.create( | |
model="gpt-3.5-turbo", | |
messages=[ | |
{"role": "system", "content": "You are a helpful coding and learning assistant."}, |
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import openai | |
import streamlit as st | |
import os | |
st.title("ChatGPT Like APP") | |
openai.api_key = os.environ["OPENAI_API_KEY"] | |
#if "openai_model" not in st.session_state: | |
st.session_state["openai_model"] = st.sidebar.selectbox("Select your openai model!",["gpt-3.5-turbo", "gpt-4"]) |
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# 1. Fetch a random paper from arxiv in the fields of: machine learning, AI, nlp, computer vision etc. | |
import arxiv | |
import random | |
import time | |
import requests | |
def fetch_paper(title): | |
""" |
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import argparse | |
from langchain.document_loaders import YoutubeLoader | |
from langchain.indexes import VectorstoreIndexCreator | |
def main(): | |
parser = argparse.ArgumentParser(description='Query YouTube videos using ChatGPT with langchain package.') | |
parser.add_argument('url', help='YouTube video URL') | |
parser.add_argument('query', help='The query you want to ask about the video') | |
parser.add_argument('-a', '--add_video_info', action='store_true', help='Add video information (default: False)') |
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DELETE ME - Must create a new gist with an initial file |
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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="UTF-8"> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<title>Learning Feed</title> | |
<style> | |
body { | |
font-family: Arial, sans-serif; | |
max-width: 800px; |
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from flask import Flask, render_template, request | |
import openai | |
import os | |
# Set up the API key | |
openai.api_key = "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" | |
app = Flask(__name__) | |
@app.route('/', methods=['GET', 'POST']) |
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from flask import Flask, render_template, request | |
import openai | |
import os | |
# Set up the API key | |
openai.api_key = "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" | |
app = Flask(__name__) | |
@app.route('/', methods=['GET', 'POST']) |
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from tensorflow.keras.models import load_model | |
import numpy as np | |
import cv2 | |
import random | |
import glob | |
import pickle | |
def show_image(img,title=""): | |
cv2.imshow(title, img) |