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import json
import requests
import gradio as gr
import random
import time
import os
import datetime
from datetime import datetime
print('for update')
API_TOKEN = os.getenv("API_TOKEN")
DECODEM_TOKEN=os.getenv("DECODEM_TOKEN")
from huggingface_hub import InferenceApi
inference = InferenceApi("bigscience/bloom",token=API_TOKEN)
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
url_decodemprompts='https://us-central1-createinsightsproject.cloudfunctions.net/getdecodemprompts'
data={"prompt_type":'ad_text_prompt',"decodem_token":DECODEM_TOKEN}
try:
r = requests.post(url_decodemprompts, data=json.dumps(data), headers=headers)
except requests.exceptions.ReadTimeout as e:
print(e)
#print(r.content)
prompt_text=str(r.content, 'UTF-8')
print(prompt_text)
def infer(prompt,
max_length = 250,
top_k = 0,
num_beams = 0,
no_repeat_ngram_size = 2,
top_p = 0.9,
seed=42,
temperature=0.7,
greedy_decoding = False,
return_full_text = False):
print(seed)
top_k = None if top_k == 0 else top_k
do_sample = False if num_beams > 0 else not greedy_decoding
num_beams = None if (greedy_decoding or num_beams == 0) else num_beams
no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size
top_p = None if num_beams else top_p
early_stopping = None if num_beams is None else num_beams > 0
params = {
"max_new_tokens": max_length,
"top_k": top_k,
"top_p": top_p,
"temperature": temperature,
"do_sample": do_sample,
"seed": seed,
"early_stopping":early_stopping,
"no_repeat_ngram_size":no_repeat_ngram_size,
"num_beams":num_beams,
"return_full_text":return_full_text
}
s = time.time()
response = inference(prompt, params=params)
#print(response)
proc_time = time.time()-s
#print(f"Processing time was {proc_time} seconds")
return response
def getadline(text_inp):
print(text_inp)
print(datetime.today().strftime("%d-%m-%Y"))
text = prompt+"\nInput:"+text_inp + "\nOutput:"
resp = infer(text,seed=random.randint(0,100))
generated_text=resp[0]['generated_text']
result = generated_text.replace(text,'').strip()
result = result.replace("Output:","")
parts = result.split("###")
topic = parts[0].strip()
topic="\n".join(topic.split('\n')[:3])
response_nsfw = requests.get('https://github.com/coffee-and-fun/google-profanity-words/raw/main/data/list.txt')
data_nsfw = response_nsfw.text
nsfwlist=data_nsfw.split('\n')
nsfwlowerlist=[]
for each in nsfwlist:
if each!='':
nsfwlowerlist.append(each.lower())
nsfwlowerlist.extend(['bra','gay','lesbian',])
print(topic)
foundnsfw=0
for each_word in nsfwlowerlist:
if each_word in topic.lower() or each_word in text_inp :
foundnsfw=1
if foundnsfw==1:
topic="Unsafe content found. Please try again with different prompts."
print(topic)
return(topic)
with gr.Blocks() as demo:
gr.Markdown("<h1><center>Market Sizing Framework for Your Business</center></h1>")
gr.Markdown(
"""ChatGPT based Insights from <a href="https://www.decodem.ai">Decodem.ai</a> for businesses.\nWhile ChatGPT has multiple use cases we have evolved specific use cases/ templates for businesses \n\n This template provides ideas on how a business can size a market they are entering. Enter a business area to size and get the results. Use examples as a guide. We use a equally powerful AI model bigscience/bloom."""
)
textbox = gr.Textbox(placeholder="Enter market size focus for business here...", lines=1,label='Your business area')
btn = gr.Button("Generate")
#output1 = gr.Textbox(lines=2,label='Market Sizing Framework')
output_image = gr.components.Image(label="Image")
btn.click(getideas,inputs=[textbox], outputs=[output_image])
examples = gr.Examples(examples=['ice cream parlor in London','HR saas for fintech','book shops in NYC','Starbucks cafe in Bangalore','organic vegetables via ecommerce','grocery delivery'],
inputs=[textbox])
demo.launch() |