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import json |
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import requests |
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import gradio as gr |
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import random |
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import time |
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import os |
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import datetime |
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from datetime import datetime |
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API_TOKEN = os.getenv("API_TOKEN") |
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from huggingface_hub import InferenceApi |
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inference = InferenceApi("bigscience/bloom",token=API_TOKEN) |
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DECODEM_TOKEN=os.getenv("DECODEM_TOKEN") |
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headers = {'Content-type': 'application/json', 'Accept': 'text/plain'} |
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url_decodemprompts='https://us-central1-createinsightsproject.cloudfunctions.net/getdecodemprompts' |
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data={"prompt_type":'devils_advocate',"decodem_token":DECODEM_TOKEN} |
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try: |
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r = requests.post(url_decodemprompts, data=json.dumps(data), headers=headers) |
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except requests.exceptions.ReadTimeout as e: |
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print(e) |
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prompt=str(r.content, 'UTF-8') |
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def infer(prompt, |
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max_length = 250, |
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top_k = 0, |
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num_beams = 0, |
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no_repeat_ngram_size = 2, |
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top_p = 0.9, |
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seed=42, |
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temperature=0.7, |
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greedy_decoding = False, |
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return_full_text = False): |
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print(seed) |
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top_k = None if top_k == 0 else top_k |
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do_sample = False if num_beams > 0 else not greedy_decoding |
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num_beams = None if (greedy_decoding or num_beams == 0) else num_beams |
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no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size |
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top_p = None if num_beams else top_p |
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early_stopping = None if num_beams is None else num_beams > 0 |
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params = { |
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"max_new_tokens": max_length, |
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"top_k": top_k, |
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"top_p": top_p, |
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"temperature": temperature, |
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"do_sample": do_sample, |
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"seed": seed, |
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"early_stopping":early_stopping, |
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"no_repeat_ngram_size":no_repeat_ngram_size, |
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"num_beams":num_beams, |
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"return_full_text":return_full_text |
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} |
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s = time.time() |
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response = inference(prompt, params=params) |
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proc_time = time.time()-s |
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return response |
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def getdevilsadvocate(text_inp): |
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print(text_inp) |
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print(datetime.today().strftime("%d-%m-%Y")) |
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text = prompt+"\nInput:"+text_inp + "\nOutput:" |
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resp = infer(text,seed=random.randint(0,100)) |
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generated_text=resp[0]['generated_text'] |
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result = generated_text.replace(text,'').strip() |
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result = result.replace("Output:","") |
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parts = result.split("###") |
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topic = parts[0].strip() |
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topic="\n".join(topic.split('\n')[:3]) |
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print(topic) |
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return(topic) |
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with gr.Blocks() as demo: |
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gr.Markdown("<h1><center>Devil's Advocate</center></h1>") |
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gr.Markdown( |
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"""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 a devil's advocate view for your ideas. Enter a crisp idea (2-3 words) and get the results. Use examples to guide. We use a equally powerful AI model bigscience/bloom.""" |
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) |
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textbox = gr.Textbox(placeholder="Enter the crisp idea here...", lines=1,label='The Idea') |
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btn = gr.Button("Generate") |
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output1 = gr.Textbox(lines=2,label="Devil's Advocate") |
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btn.click(getdevilsadvocate,inputs=[textbox], outputs=[output1]) |
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examples = gr.Examples(examples=['paneer donuts','smart tee shirt','blockchain for EV chargers','autonomous cars'], |
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inputs=[textbox]) |
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demo.launch() |
