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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("<h1><center>Distracted Boyfriend Meme😄- Using Bloom 🌸 </center></h1>")
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<pre> Bloom generating very few tokens When Few words are Enough</pre>\n\n<pre><span style='font-size:30px'> 🤝Memes</span></pre>\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)"""
)
# <span style="color:green;font-weight:700;font-size:20px">markdown color font styles</span>
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)