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)