app.py

import gradio as gr
import requests
import os 

##Bloom
API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
HF_TOKEN = os.environ["HF_TOKEN"]
headers = {"Authorization": f"Bearer {HF_TOKEN}"}

#Testing various prompts initially
prompt1 = """
word: risk
poem using word: And then the day came,
when the risk
to remain tight
in a bud
was more painful
than the risk
it took
to blossom.
word: """

prompt2 = """
Q: Joy has 5 balls. He buys 2 more cans of balls. Each can has 3 balls. How many balls he has now?
A: Joy had 5 balls. 2 cans of 3 balls each is 6 balls. 5 + 6 = 11. Answer is 11.
Q: Jane has 16 balls. Half balls are golf balls, and half golf balls are red. How many red golf balls are there?
A: """

prompt3 = """Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?
A: Let’s think step by step.
"""


def text_generate(prompt):
  
  #prints for debug
  print(f"*****Inside poem_generate - Prompt is :{prompt}")
  json_ = {"inputs": prompt,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          #"max_new_tokens": 250,
          "return_full_text": True
          }}
  response = requests.post(API_URL, headers=headers, json=json_)
  print(f"Response  is : {response}")
  output = response.json()
  print(f"output is : {output}") #{output}")
  output_tmp = output[0]['generated_text']
  print(f"output_tmp is: {output_tmp}")
  solution = output_tmp.split("\nQ:")[0]   #output[0]['generated_text'].split("Q:")[0] # +"."
  print(f"Final response after splits is: {solution}")
 
  return solution 


demo = gr.Blocks()

with demo:
  gr.Markdown("<h1><center>Step By Step With Bloom</center></h1>")
  gr.Markdown(
        """ [BigScienceW Bloom](https://twitter.com/BigscienceW) \n\n Large language models have demonstrated a capability of 'Chain-of-thought reasoning'. Some amazing researchers( [Jason Wei et al.](https://arxiv.org/abs/2206.07682)) recently found out that by addding **Lets think step by step** it improves the model's zero-shot performance. Some might say — You can get good results out of LLMs if you know how to speak to them."""
        )
  with gr.Row():
    example_prompt = gr.Radio( ["Q: A juggler can juggle 16 balls. Half of the balls are golf balls, and half of the golf balls are blue. How many blue golf balls are there?\nA: Let’s think step by step.\n", "Q: Roger has 5 tennis balls already. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?\nA: Let’s think step by step.\n", "Q: On an average Joe throws 25 punches per minute. His fight lasts 5 rounds of 3 minutes each. How many punches did he throw?\nA: Let’s think step by step.\n"], label= "Choose a sample Prompt")
    #input_word = gr.Textbox(placeholder="Enter a word here to generate text ...")
    generated_txt = gr.Textbox(lines=7)
 
  
  b1 = gr.Button("Generate Text")
  b1.click(text_generate,inputs=example_prompt, outputs=generated_txt) 
 
demo.launch(enable_queue=True, debug=True)