import gradio as gr import requests import os import PIL from PIL import Image from PIL import ImageDraw from PIL import ImageFont ##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. """ prompt = """Distracted from hubble by james webb. Distracted from homework by side projeect. Distracted from goals by new goals. Distracted from """ def write_on_image(final_solution): print("************ Inside write_on_image ***********") image_path0 = "./distracted0.jpg" image0 = Image.open(image_path0) I1 = ImageDraw.Draw(image0) myfont = ImageFont.truetype('./font1.ttf', 30) I1.text((613, 89), "girlfriend",font=myfont, fill =(255, 255, 255)) I1.text((371, 223), "ME", font=myfont, fill =(255, 255, 255)) I1.text((142, 336), "new girl",font=myfont, fill =(255, 255, 255)) return image0 def meme_generate(img): #prompt, generated_txt): #, input_prompt_sql ): #, input_prompt_dalle2): print(f"*****Inside meme_generate - Prompt is :{prompt}") #if input_prompt_sql != '': # prompt = input_prompt_sql #"Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: " +input_prompt_sql + "\nPostgreSQL query: " #elif 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": 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 is : {new_prompt}") #print(f"display_output for printing on screen is : {display_output}") #if len(new_prompt) == 0: # temp_text = display_output[::-1] # print(f"What is the 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"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}") # display_output = display_output[:-1] meme_image = write_on_image(final_solution) return meme_image #final_solution #display_output, new_prompt #generated_txt+prompt demo = gr.Blocks() with demo: gr.Markdown("