app.py

import gradio as gr
import requests
import os 

##Bloom Inference API

API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" # Models on HF feature inference API which allows direct call and easy interface

HF_TOKEN = os.environ["HF_TOKEN"] # Add a token called HF_TOKEN under profile in settings access tokens.  Then copy it to the repository secret in this spaces settings panel.  os.environ reads from there.

# For headers the bearer token needs to incclude your HF_TOKEN value.
headers = {"Authorization": f"Bearer {HF_TOKEN}"}

# Improved text generation function
def text_generate(prompt, generated_txt):
    # Initialize Thoughts variable to aggregate text
    Thoughts = ""

    # Debug: display the prompt
    Thoughts += f"Prompt: {prompt}\n"

    json_ = {
        "inputs": prompt,
        "parameters": {
            "top_p": 0.9,
            "temperature": 1.1,
            "return_full_text": True,
            "do_sample": True,
        },
        "options": {
            "use_cache": True,
            "wait_for_model": True,
        },
    }
    response = requests.post(API_URL, headers=headers, json=json_)
    output = response.json()

    # Debug: display the output
    Thoughts += f"Output: {output}\n"
    output_tmp = output[0]['generated_text']

    # Debug: display the output_tmp
    Thoughts += f"output_tmp is: {output_tmp}\n"
    solution = output_tmp.split("\nQ:")[0]

    # Debug: display the solution after splitting
    Thoughts += f"Final response after splits is: {solution}\n"

    if '\nOutput:' in solution:
        final_solution = solution.split("\nOutput:")[0]
        Thoughts += f"Response after removing output is: {final_solution}\n"
    elif '\n\n' in solution:
        final_solution = solution.split("\n\n")[0]
        Thoughts += f"Response after removing new line entries is: {final_solution}\n"
    else:
        final_solution = solution

    if len(generated_txt) == 0:
        display_output = final_solution
    else:
        display_output = generated_txt[:-len(prompt)] + final_solution

    new_prompt = final_solution[len(prompt):]

    # Debug: display the new prompt for the next cycle
    Thoughts += f"new prompt for next cycle is: {new_prompt}\n"
    Thoughts += f"display_output for printing on screen is: {display_output}\n"

    if len(new_prompt) == 0:
        temp_text = display_output[::-1]
        Thoughts += f"What is the last character of the sentence?: {temp_text[0]}\n"

        if temp_text[1] == '.':
            first_period_loc = temp_text[2:].find('.') + 1
            Thoughts += f"Location of last Period is: {first_period_loc}\n"
            new_prompt = display_output[-first_period_loc:-1]
            Thoughts += f"Not sending blank as prompt so new prompt for next cycle is: {new_prompt}\n"
        else:
            first_period_loc = temp_text.find('.')
            Thoughts += f"Location of last Period is: {first_period_loc}\n"
            new_prompt = display_output[-first_period_loc:-1]
            Thoughts += f"Not sending blank as prompt so new prompt for next cycle is: {new_prompt}\n"

        display_output = display_output[:-1]

    return display_output, new_prompt, Thoughts



    
# Text generation
def text_generate_old(prompt, generated_txt): 
  #Prints to debug the code
  print(f"*****Inside text_generate - Prompt is :{prompt}")
  json_ = {"inputs": prompt,
            "parameters":
            {
            "top_p": 0.9,
          "temperature": 1.1,
          #"max_new_tokens": 64,
          "return_full_text": True,
          "do_sample":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}")

    
  if '\nOutput:' in solution:
    final_solution = solution.split("\nOutput:")[0] 
    print(f"Response after removing output is: {final_solution}")
  elif '\n\n' in solution:
    final_solution = solution.split("\n\n")[0] 
    print(f"Response after removing new line entries is: {final_solution}")
  else:
    final_solution = solution
  if len(generated_txt) == 0 :
    display_output = final_solution
  else:
    display_output = generated_txt[:-len(prompt)] + final_solution

      
  new_prompt = final_solution[len(prompt):]
  print(f"New prompt for next cycle: {new_prompt}")
  print(f"Output final is : {display_output}")
  if len(new_prompt) == 0:
    temp_text = display_output[::-1]
    print(f"Last character of sentence: {temp_text[0]}")
    if temp_text[1] == '.':
      first_period_loc = temp_text[2:].find('.') + 1
      print(f"Location of last Period is: {first_period_loc}")
      new_prompt = display_output[-first_period_loc:-1]
      print(f"Not sending blank as prompt so new prompt for next cycle is : {new_prompt}")
    else:
      print("HERE")
      first_period_loc = temp_text.find('.')
      print(f"Last Period is : {first_period_loc}")
      new_prompt = display_output[-first_period_loc:-1]
      print(f"New prompt for next cycle is : {new_prompt}")
    display_output = display_output[:-1]
  return display_output, new_prompt  

# An insightful and engaging self-care health care demo
demo = gr.Blocks()

with demo:
    with gr.Row():
        input_prompt = gr.Textbox(
            label="Write a self-care or health care related question to get started...",
            lines=3,
            value="Dear AI, please tell me about the importance of self-care and how it contributes to overall health and well-being.",
        )

    with gr.Row():
        generated_txt = gr.Textbox(lines=5, visible=True)

    with gr.Row():
        Thoughts = gr.Textbox(lines=10, visible=True)

    gen = gr.Button("Discover Health Insights")

    gen.click(
        text_generate,
        inputs=[input_prompt, generated_txt],
        outputs=[generated_txt, input_prompt, Thoughts],
    )

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