Create app.py

app.py

@@ -0,0 +1,177 @@
+import json
+import requests
+import gradio as gr
+import random
+import time
+import os
+import datetime
+from datetime import datetime
+from PIL import Image
+from PIL import ImageOps
+from PIL import Image, ImageDraw, ImageFont
+from textwrap import wrap
+import json
+from io import BytesIO
+
+print('for update')
+
+API_TOKEN = os.getenv("API_TOKEN")
+HRA_TOKEN=os.getenv("HRA_TOKEN")
+
+
+from huggingface_hub import InferenceApi
+inference = InferenceApi("bigscience/bloom",token=API_TOKEN)
+
+headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
+url_hraprompts='https://us-central1-createinsightsproject.cloudfunctions.net/gethrahfprompts'
+
+data={"prompt_type":'stable_diffusion_tee_shirt_text',"hra_token":HRA_TOKEN}
+try:
+    r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers)
+except requests.exceptions.ReadTimeout as e:
+    print(e)
+#print(r.content)
+
+
+prompt_text=str(r.content, 'UTF-8')
+print(prompt_text)
+data={"prompt_type":'stable_diffusion_tee_shirt_image',"hra_token":HRA_TOKEN}
+try:
+    r = requests.post(url_hraprompts, data=json.dumps(data), headers=headers)
+except requests.exceptions.ReadTimeout as e:
+    print(e)
+#print(r.content)
+
+prompt_image=str(r.content, 'UTF-8')
+print(prompt_image)
+
+ENDPOINT_URL="https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1" # url of your endpoint
+#ENDPOINT_URL="https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-1-5" # url of your endpoint
+HF_TOKEN=API_TOKEN# token where you deployed your endpoint
+
+def generate_image(prompt_SD:str):
+    payload = {"inputs": prompt_SD,}
+    headers = {
+        "Authorization": f"Bearer {HF_TOKEN}",
+        "Content-Type": "application/json",
+        "Accept": "image/png" # important to get an image back
+    }
+    response = requests.post(ENDPOINT_URL, headers=headers, json=payload)
+    #print(response.content)
+    img = Image.open(BytesIO(response.content))
+
+    return img
+    
+def infer(prompt,
+          max_length = 250,
+          top_k = 0,
+          num_beams = 0,
+          no_repeat_ngram_size = 2,
+          top_p = 0.9,
+          seed=42,
+          temperature=0.7,
+          greedy_decoding = False,
+          return_full_text = False):
+    
+    print(seed)
+    top_k = None if top_k == 0 else top_k
+    do_sample = False if num_beams > 0 else not greedy_decoding
+    num_beams = None if (greedy_decoding or num_beams == 0) else num_beams
+    no_repeat_ngram_size = None if num_beams is None else no_repeat_ngram_size
+    top_p = None if num_beams else top_p
+    early_stopping = None if num_beams is None else num_beams > 0
+
+    params = {
+        "max_new_tokens": max_length,
+        "top_k": top_k,
+        "top_p": top_p,
+        "temperature": temperature,
+        "do_sample": do_sample,
+        "seed": seed,
+        "early_stopping":early_stopping,
+        "no_repeat_ngram_size":no_repeat_ngram_size,
+        "num_beams":num_beams,
+        "return_full_text":return_full_text
+    }
+    
+    s = time.time()
+    response = inference(prompt, params=params)
+    #print(response)
+    proc_time = time.time()-s
+    #print(f"Processing time was {proc_time} seconds")
+    return response
+
+def getadline(text_inp):
+    print(text_inp)
+    print(datetime.today().strftime("%d-%m-%Y"))
+  
+    text = prompt_text+"\nInput:"+text_inp + "\nOutput:"
+    resp = infer(text,seed=random.randint(0,100))
+    
+    generated_text=resp[0]['generated_text']
+    result = generated_text.replace(text,'').strip()
+    result = result.replace("Output:","")
+    parts = result.split("###")
+    topic = parts[0].strip()
+    topic="\n".join(topic.split('\n'))
+    
+    response_nsfw = requests.get('https://github.com/coffee-and-fun/google-profanity-words/raw/main/data/list.txt')
+    data_nsfw = response_nsfw.text
+    nsfwlist=data_nsfw.split('\n')
+    nsfwlowerlist=[]
+    for each in nsfwlist:
+      if each!='':
+        nsfwlowerlist.append(each.lower())
+    nsfwlowerlist.extend(['bra','gay','lesbian',])
+    print(topic)
+    mainstring=text_inp
+    foundnsfw=0
+    for each_word in nsfwlowerlist:
+        raw_search_string = r"\b" + each_word + r"\b"
+        match_output = re.search(raw_search_string, mainstring)
+        no_match_was_found = ( match_output is None )
+        if no_match_was_found:
+          foundnsfw=0
+        else:
+          foundnsfw=1
+          print(each_word)
+          break
+    if foundnsfw==1:
+        topic="Unsafe content found. Please try again with different prompts."
+        print(topic)
+    return(topic)
+
+def getadvertisement(topic):
+    if topic!='':
+        input_keyword=topic
+    else:
+        input_keyword=getadline(random.choice('abcdefghijklmnopqrstuvwxyz'))
+        if 'Unsafe content found' in input_keyword:
+            input_keyword='Abstarct art with splash of colors'
+    prompt_SD=input_keyword+','+prompt_image
+    # generate image
+    image = generate_image(prompt_SD)
+    
+    # save to disk
+    image.save("finalimage.png")
+    
+    return 'finalimage.png'
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>Tee Shirt Designs</center></h1>")
+    gr.Markdown(
+        """Enter a prompt  and get the tee shirt design. Use examples as a guide. We use an equally powerful AI model bigscience/bloom."""
+        )
+    textbox = gr.Textbox(placeholder="Enter prompt...", lines=1,label='Your prompt')
+    btn = gr.Button("Generate")    
+    #output1 = gr.Textbox(lines=2,label='Market Sizing Framework')
+    output_image = gr.components.Image(label="Your tee shirt")
+
+
+    btn.click(getadvertisement,inputs=[textbox], outputs=[output_image])
+    examples = gr.Examples(examples=['anime art of man fighting','intricate skull','heavy metal band cover','abstract art of plants',],
+                           inputs=[textbox])
+    
+
+demo.launch()