pages/entity_extraction.py

from openai import OpenAI
client = OpenAI()
import streamlit as st
from streamlit import session_state
import json
import moviepy.editor as mp
from io import StringIO
import openai
import json
import os
import streamlit as st
import cv2 as cv
import tempfile
import os
os.environ['OPENAI_API_KEY'] = "sk-proj-ZbejHdD4ZgJ5FFJ6LjMNT3BlbkFJ1WHLrJMFL03D8cMWSoFY"
openai.api_key = os.environ['OPENAI_API_KEY']
def openai1(text1):
    response = client.chat.completions.create(
      model="gpt-4",
      messages=[
        {
          "role": "system",
          "content": "Identify the sentiment, whether sexual content not, abuse or not, related to academics or not (how frequent the conversation is related to non academic topics give it in percentage) and tone and phone number, name of teacher if mentioned, student feedback if mentioned and Whether the conversation is academic or non academic. Also add teacher behavior as positive and negative. \n<<REMEMBER>>\nGive output in json. generate a score 0 and 1. If Negative then 1 and if it's positive then it's 0. If the tone is aggresive then it should be 1. If phone number and email is given then it's 1.  And academic should be as percentage. generate the total score and add academic percentage also.  "
        },
        {
          "role": "user",
          "content": text1}
      ],
      temperature=1,
      max_tokens=256,
      top_p=1,
      frequency_penalty=0,
      presence_penalty=0
    )
    return response.choices[0].message.content

st.write("# Teacher session evaluation! 👋")
f = st.file_uploader("Upload file")
if f is not None:

    # To convert to a string based IO:

    tfile = tempfile.NamedTemporaryFile(delete=False)
    tfile.write(f.read())
    clip = mp.VideoFileClip(tfile.name)
    #         if clip.duration <=60:
    clip.audio.write_audiofile("theaudio.mp3")
    #         else:
            #clip.subclip(0,60).write_audiofile("theaudio.mp3")
    #         try:
    audio_file= open("theaudio.mp3", "rb")
    transcript_english = client.audio.translations.create(
                 model="whisper-1", 
                 file=audio_file,temperature = 0).text
    print(transcript_english)
    st.text_area("Report", value = openai1(transcript_english))