app.py

import gradio as gr
import requests
import os

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

def text_generate(prompt):
  print(f"Prompt is :{prompt}")
  p =  prompt + " Solution: " 
  print(f"Final prompt is : {p}")
  json_ = {"inputs": p,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          "max_new_tokens": 250,
          "return_full_text": True
          }, "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_tmp = output[0]['generated_text']
  print(f"output_tmp is: {output_tmp}")
  solution = output_tmp.split("\nQ:")[0]
  print(f"Final response after splits is: {solution}") 
  return solution 

demo = gr.Blocks()

with demo:
    gr.Markdown("<h1><center>Length generalization (LG) With BLOOM🌸 </center></h1>")
    gr.Markdown(
            """
            We will examine large language models ability to extrapolate to longer problems! \n
            Length generalization (LG) is important: Often, long examples are rare and intrinsically more difficult, yet are the ones we care more about.  \n
            Recent paper [Exploring Length Generalization in Large Language Models](https://arxiv.org/pdf/2207.04901) found that using few-shot  [scratchpad](https://arxiv.org/abs/2112.00114), a combo behind many strong LLM results (eg. #Minerva ) \n
            leads to **substantial improvements in length generalization!** \n
            In-context learning enables variable length pattern matching, producing solutions of correct lengths. \n
            This space is an attempt at inspecting this LLM behavior/capability in the new HuggingFace BigScienceW [Bloom](https://huggingface.co/bigscience/bloom) model. \n
            This Space is created by [Muhtasham Oblokulov](https://twitter.com/muhtasham9) for EuroPython 2022 Demo. \n
            This Space is work in progress, BLOOM doesn't support inference on long sequencess so you may try with shorter sequences. \n
            """
            )
    with gr.Row(): 
        input_prompt = gr.Textbox(value="Q:The coin is heads up.(1) Then Austin flips. Is the coin still heads up? Solution: Coin is initially heads up. (1) After Austin flips, coin turns to heads. Q: The coin is heads up. (2) Then Austin doesn't flip. (1) Then Kara flips. Is the coin still heads up?",
        label="Enter your examples zero-shot (few-shot is not supported due to API limit) followed by Query :")
        generated_txt = gr.Textbox(lines=10, label="Generated Solution:")

    b1 = gr.Button("Generate Text")
    b1.click(text_generate,inputs=[input_prompt], outputs=[generated_txt])
    
    with gr.Row(): 
            gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=europython2022_scratchpad-w-bloom)")

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