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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)
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