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import streamlit as st | |
from streamlit import session_state | |
import os | |
import openai | |
openai.api_key = os.getenv("OPENAI_API_KEY") | |
import pandas as pd | |
from sklearn.preprocessing import LabelEncoder | |
import numpy as np | |
def gpt4_score(schema, query): | |
response = openai.ChatCompletion.create( | |
model="gpt-4", | |
messages=[ | |
{ | |
"role": "system", | |
"content": "You are Code generator assistant. your task is to generate a code/query based on the instructions given in any language.\nif it's sql then accept query instruction also.\n\n<<REMEMBER>> Give only code or query. Don't provide any extra information.\n\n<<OUTPUT>>" | |
}, | |
{ | |
"role": "user", | |
"content": f"Schema/ Detail: {schema}" | |
}, | |
{ | |
"role": "user", | |
"content": f"Query/instruction: {query}" | |
} | |
], | |
temperature=0.7, | |
max_tokens=701, | |
top_p=1, | |
frequency_penalty=0, | |
presence_penalty=0 | |
) | |
return response.choices[0].message.content | |
st.write("# Auto Code Generation! 👋") | |
if 'score' not in session_state: | |
session_state['score']= "" | |
text1= st.text_area(label= "Please write the Schema or Detailed explaination bellow", | |
placeholder="What does the text say?") | |
text2= st.text_area(label= "Please write the Query or code instructions bellow", | |
placeholder="What does the text say?") | |
def classify(text1,text2): | |
session_state['score'] = gpt4_score(text1,text2) | |
st.text_area("result", value=session_state['score']) | |
st.button("Classify", on_click=classify, args=[text1,text2]) |