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}"}


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, input_prompt_sql, input_prompt_dalle2):

  print(f"*****Inside poem_generate - Prompt is :{prompt}")
  if input_prompt_sql != '':
    prompt = "Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: " +input_prompt_sql + "\nPostgreSQL query: "
  else if input_prompt_dalle2 !='':
    prompt = "Dalle Prompt: " + input_prompt_dalle2 + "\nNew Dalle Prompt: "
    
  json_ = {"inputs": prompt,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          "max_new_tokens": 250,
          "return_full_text": False,
          }, 
          "options": 
          {"use_cache": True,
          "wait_for_model": 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}")
  #if "\n\n" not in output_tmp:
  #  if output_tmp.find('.') != -1:
  #    idx = output_tmp.find('.')
  #    poem = output_tmp[:idx+1]
  #  else:
  #    idx = output_tmp.rfind('\n')
  #    poem = output_tmp[:idx]
  #else:
  #  poem = output_tmp.split("\n\n")[0] # +"."
  #poem = poem.replace('?','')
  #print(f"Poem being returned is: {poem}")
  return solution #output #response #output_tmp #poem


demo = gr.Blocks()

with demo:
  gr.Markdown("<h1><center>Bloom</center></h1>")
  gr.Markdown(
        """Testing Bloom for SQL generation """
        )
  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"], label= "Choose a sample Prompt")
    example_prompt = gr.Radio( ["Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: how many users signed up in the past month?\nPostgreSQL query: ", "Dalle Prompt: Cyberwave vaporpunk art of a kneeling figure, looking up at a glowing neon book icon, smoke and mist, pink and blue lighting, cybernetic sci-fi render\nNew Dalle Prompt: " ], label= "Choose a sample Prompt")
    
    input_prompt_sql = gr.Textbox(label="Or Write text to get SQL commands...")
    input_prompt_dalle2 = gr.Textbox(label="Or Write sample Dalle2 prompts to get more Prompt ideas...")
    
    #input_word = gr.Textbox(placeholder="Enter a word here to generate text ...")
    generated_txt = gr.Textbox(lines=7)
    #output_image = gr.Image(type="filepath", shape=(256,256))
  
  b1 = gr.Button("Generate SQL")
  #b2 = gr.Button("Generate Image") 

  b1.click(text_generate,inputs=[example_prompt, input_prompt_sql, input_prompt_dalle2], outputs=generated_txt) #input_word
  #b2.click(poem_to_image, poem_txt, output_image)
  #examples=examples 

demo.launch(enable_queue=True, debug=True)