Spaces:
Sleeping
Sleeping
File size: 3,420 Bytes
abee9b3 |
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 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
from flask import Flask, render_template, request, send_from_directory
import requests
with open('token.txt', 'r', encoding='utf-8') as file:
imageG = file.read()
with open('libyan.json', 'r', encoding='utf-8') as file:
ly = file.read()
with open('train.json', 'r', encoding='utf-8') as file:
img = file.read()
app = Flask(__name__)
def get_assistant_response(user_input):
payload = {
"mode": "chat",
"chat_history": conversation_history,
"data": {
"query": f"at night without light there some body call Rizon \n Rizon open the door and found Elon to talk with him , to be more real Use alot of Emotions like uh umm uhh ahh umm etc of this don't take my this example as role but just do it \n \n Rizon : {user_input} \n Elon :",
"loader": "PDFReader",
"text":""
}
}
response = requests.post(url2, headers=headers, json=payload)
data = response.json()
# Extract the response from the data
response_text = data["data"]["response"]
response_text = response_text.strip().replace('\n', '<br>')
# Check if the response contains ~
if "~" in response_text:
conversation_history.append({"\n ALex-9": response_text})
# Extract the prompt between ~~
prompt_start = response_text.index("~") + 1
prompt_end = response_text.index("~", prompt_start)
prompt = response_text[prompt_start:prompt_end]
# Call the text-to-image API
image_url = generate_image(prompt)
response_text += f"<br><br><img src='{image_url}'>"
# Delete the prompt from the response text
response_text = response_text.replace("~" + prompt + "~", "")
prompt = response_text
return response_text
def generate_image(prompt):
url = "https://api.braininc.net/be/lambda/function/stableai"
headers = {
"Authorization": "token 72ec00483379076f580eb8126f29da802a5140c3",
"Content-Type": "application/json",
}
payload = {
"json": True,
"prompt": f"{prompt} Realastic Photo 4K",
"public_topic": "/studios/516104/wsuid_new-edge-4_nodeid_editor-4/imageGen/1719250632789"
}
response = requests.post(url, headers=headers, json=payload)
data = response.json()
cdn_url = data["cdn_url"]
return cdn_url
url2 = "https://api.braininc.net/be/vectordb/indexers/"
headers = {
"Authorization": "token 72ec00483379076f580eb8126f29da802a5140c3",
"Content-Type": "application/json;charset=UTF-8",
}
conversation_history = []
@app.route('/')
def home():
global conversation_history
conversation_history = []
return render_template('ai-chat-bot.html')
@app.route('/get_response', methods=['POST'])
def get_response():
user_input = request.form['user_input']
conversation_history.append({
"role": "user",
"content":f"Rizon : {user_input}",
"additional_kwargs": {}
})
if user_input.lower() == "exit":
return "exit"
response_text = get_assistant_response(user_input)
conversation_history.append({
"role": "assistant",
"content": f"Elon : {response_text}",
"additional_kwargs": {}
})
return response_text
@app.route('/desine/<path:path>')
def send_static(path):
return send_from_directory('desine', path)
if __name__ == '__main__':
app.run(host='0.0.0.0',port = 7860)
|