File size: 1,889 Bytes
0275e43
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
import os
import json
import openai
import gradio as gr
import firebase_admin
from firebase_admin import credentials, firestore

# Get the service account key from the environment variable
service_account_key = os.environ["firebasekey"]

# Parse the service account key into a dictionary
service_account_info = json.loads(service_account_key)

# Create a Certificate object from the service account info
cred = credentials.Certificate(service_account_info)

# Initialize the Firebase Admin SDK
firebase_admin.initialize_app(cred)

# # Create a reference to the Firestore database
db = firestore.client()

openai.api_key = os.environ.get("openai_api_key")

def store_message(user_input, completion):
    new_completion = db.collection('prayerGeneratorCompletions').document()
    new_completion.set({
        'user_input': user_input,
        'completion': completion,
        'created_time': firestore.SERVER_TIMESTAMP,
        'model': 'text-davinci-003',
        'temperature': 0.7,
        'title': 'Talk to God'
    })

# Prayer generator
def greet(input):

    myInput = input
    myPrompt = f"Respond as a compassionate pastor with an in-depth christian prayer that address details of the prayer request, the setting, people inovlved. In your response cite a bible verse from the christian bible that is specifically relevant, and explain it's relevance.Then end the response with a closing prayer.  Human: {myInput} \n\n Pastor:"
    response = openai.Completion.create(
        model="text-davinci-003",
        prompt=myPrompt,
        temperature=0.7,
        max_tokens=3000,
        top_p=1.0,
        frequency_penalty=0.0,
        presence_penalty=0.0
    )
    raw_response = response['choices'][0]['text']
    print(raw_response)
    store_message(myInput, raw_response)
    return raw_response


demo = gr.Interface(fn=greet, inputs="text", outputs="text")

demo.launch()