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}"} 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) prompt_list = final_solution.split('\n') girlfriend = prompt_list[8].split(':')[1].strip() girlfriend_list = girlfriend.split() if len(girlfriend_list) >= 2: girlfriend = '\n'.join(girlfriend_list) print(f"girlfriend is : {girlfriend }") new_girl = prompt_list[9].split(':')[1].strip() new_girl_list = new_girl.split() if len(new_girl_list) > 2: new_girl = '\n'.join(new_girl_list) print(f"new_girl is : {new_girl}") prompt_list.pop(0) prompt_list.pop(0) prompt_list = prompt_list[:8] prompt_list.append('Distracted from:') print(f"prompt list is : {prompt_list}") new_prompt = '\n'.join(prompt_list) print(f"final_solution is : {new_prompt}") 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, new_prompt def meme_generate(img, prompt, temp, top_p): #prompt, generated_txt): #, input_prompt_sql ): #, input_prompt_dalle2): print(f"*****Inside meme_generate - Prompt is :{prompt}") if len(prompt) == 0: prompt = """Distracted from: homework\nby: side project\nDistracted from: goals\nby: new goals\nDistracted from: working hard\nby: hardly working\nDistracted from: twitter\nby: open in browser\nDistracted from:""" json_ = {"inputs": prompt, "parameters": { "top_p": top_p, #0.90 default "max_new_tokens": 64, "temperature": temp, #1.1 default "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}") meme_image, new_prompt = write_on_image(solution) return meme_image, new_prompt demo = gr.Blocks() with demo: gr.Markdown("

Distracted Boyfriend Meme😄- Using Bloom 🌸

") gr.Markdown( """Bloom is a model made by research teams from [HuggingFace](https://huggingface.co/bigscience/bloom) and world over (more than 1000 researchers coming together and working as [BigScienceW Bloom](https://twitter.com/BigscienceW)).Large language models can produce coherent sentences but can they produce **Humor** too? Yes, they can, given the correct prompt (And Yes, Prompt Engineering 🤖 should definitely become a thing by now).\n\n**How to Use this App**: Just Fire Away the Generate Meme button below, as many times as you want!! If you see repeated or similar memes getting generated in consecutive runs, toggle temperature and top_p values.\n\n**How this App works**: Figuring out the right set of Prompting + Writing on an Image + Bit of engineering. Currently, Bloom's Public API has size-limits on Token-Generation, so you can get only few tokens generated at a time.\n\n
                              Bloom generating very few tokens                    When Few words are Enough
\n\n
                                🤝Memes
\n\nIt is a fun little App which you can play with for a while.This Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma)""" ) # markdown color font styles with gr.Row(): in_image = gr.Image(value="./distracted0.jpg", visible=False) in_image_display = gr.Image(value="./distracted00.jpg", visible=True) input_prompt = gr.Textbox(label="Write some prompt...", lines=5, visible=False) output_image = gr.Image() with gr.Row(): in_slider_temp = gr.Slider(minimum=0.0, maximum=1.4, value=1.1, step=0.1, label='Temperature') in_slider_top_p = gr.Slider(minimum=0.50, maximum=0.99, value=0.90, step=0.01, label='Top_p') b1 = gr.Button("Generate Memes") b1.click(meme_generate, inputs=[in_image, input_prompt, in_slider_temp, in_slider_top_p] , outputs=[output_image,input_prompt]) demo.launch(enable_queue=True, debug=True)