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Update app.py
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app.py
CHANGED
@@ -6,6 +6,12 @@ import time
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import os
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import datetime
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from datetime import datetime
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print('for update')
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@@ -28,7 +34,33 @@ except requests.exceptions.ReadTimeout as e:
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prompt_text=str(r.content, 'UTF-8')
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print(prompt_text)
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def infer(prompt,
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max_length = 250,
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top_k = 0,
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@@ -100,19 +132,137 @@ def getadline(text_inp):
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print(topic)
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return(topic)
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with gr.Blocks() as demo:
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gr.Markdown("<h1><center>
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gr.Markdown(
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"""ChatGPT based Insights from <a href="https://www.decodem.ai">Decodem.ai</a> for businesses.\nWhile ChatGPT has multiple use cases we have evolved specific use cases/ templates for businesses \n\n This template provides ideas on how a business can
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)
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textbox = gr.Textbox(placeholder="Enter
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btn = gr.Button("Generate")
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#output1 = gr.Textbox(lines=2,label='Market Sizing Framework')
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output_image = gr.components.Image(label="
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btn.click(
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examples = gr.Examples(examples=['
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inputs=[textbox])
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import os
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import datetime
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from datetime import datetime
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from PIL import Image
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from PIL import ImageOps
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from PIL import Image, ImageDraw, ImageFont
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from textwrap import wrap
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import json
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from io import BytesIO
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print('for update')
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prompt_text=str(r.content, 'UTF-8')
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print(prompt_text)
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data={"prompt_type":'ad_image_prompt',"decodem_token":DECODEM_TOKEN}
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try:
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r = requests.post(url_decodemprompts, data=json.dumps(data), headers=headers)
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except requests.exceptions.ReadTimeout as e:
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print(e)
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#print(r.content)
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prompt_image=str(r.content, 'UTF-8')
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print(prompt_image)
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ENDPOINT_URL="https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1" # url of your endpoint
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#ENDPOINT_URL="https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-1-5" # url of your endpoint
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HF_TOKEN=API_TOKEN# token where you deployed your endpoint
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def generate_image(prompt_SD:str):
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payload = {"inputs": prompt_SD,}
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headers = {
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"Authorization": f"Bearer {HF_TOKEN}",
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"Content-Type": "application/json",
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"Accept": "image/png" # important to get an image back
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}
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response = r.post(ENDPOINT_URL, headers=headers, json=payload)
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print(response.content)
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img = Image.open(BytesIO(response.content))
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return img
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def infer(prompt,
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max_length = 250,
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top_k = 0,
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print(topic)
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return(topic)
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def getadvertisement(topic):
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input_keyword=topic
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backdrop=['surrounded by water droplets','in front of a brick wall','in front of a wooden wall','in a white boho style studio','with nature backdrop','with water splash','laying on a wooden table',]
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whichitem=random.randint(0,len(backdrop)-1)
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prompt_SD='product photograph of '+input_keyword+' '+backdrop[whichitem]+prompt_image
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# generate image
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image = generate_image(prompt_SD)
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# save to disk
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image.save("generation.png")
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# Set the font to be used
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req = requests.get("https://github.com/openmaptiles/fonts/raw/master/roboto/Roboto-Regular.ttf")
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FONT_USER_INFO = ImageFont.truetype(BytesIO(req.content), 75, encoding="utf-8")
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FONT_TEXT = ImageFont.truetype(BytesIO(req.content), 75, encoding="utf-8")
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TITLE_TEXT = ImageFont.truetype(BytesIO(req.content), 75, encoding="utf-8")
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#FONT_USER_INFO = ImageFont.load_default()
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#FONT_TEXT = ImageFont.load_default()
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# Image dimensions (pixels)
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WIDTH = 768
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HEIGHT = 768
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# Color scheme
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COLOR_BG = 'white'
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COLOR_NAME = 'black'
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COLOR_TAG = (64, 64, 64)
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COLOR_TEXT = 'black'
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# Write coordinates
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COORD_PHOTO = (10, 40)
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COORD_NAME = (10, 200)
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COORD_TAG = (10, 280)
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COORD_TEXT = (10, 128)
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# Extra space to add in between lines of text
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LINE_MARGIN = 5
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# -----------------------------------------------------------------------------
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# Information for the image
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# -----------------------------------------------------------------------------
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text = getadline(input_keyword)
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print(text)
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img_name = "textimage"
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# -----------------------------------------------------------------------------
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# Setup of variables and calculations
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# -----------------------------------------------------------------------------
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# Break the text string into smaller strings, each having a maximum of 37\
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# characters (a.k.a. create the lines of text for the image)
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text_string_lines = wrap(text, 10)
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# Horizontal position at which to start drawing each line of the tweet body
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x = COORD_TEXT[0]
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# Current vertical position of drawing (starts as the first vertical drawing\
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# position of the tweet body)
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y = COORD_TEXT[1]
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# Create an Image object to be used as a means of extracting the height needed\
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# to draw each line of text
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temp_img = Image.new('RGB', (0, 0))
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temp_img_draw_interf = ImageDraw.Draw(temp_img)
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# List with the height (pixels) needed to draw each line of the tweet body
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# Loop through each line of text, and extract the height needed to draw it,\
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# using our font settings
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line_height = [
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temp_img_draw_interf.textsize(text_string_lines[i], font=FONT_TEXT )[1]
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for i in range(len(text_string_lines))
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]
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# -----------------------------------------------------------------------------
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# Image creation
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# -----------------------------------------------------------------------------
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# Create what will be the final image
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img_final = Image.new('RGB', (WIDTH, HEIGHT), color='white')
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# Create the drawing interface
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draw_interf = ImageDraw.Draw(img_final)
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# Draw each line of the tweet body. To find the height at which the next\
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# line will be drawn, add the line height of the next line to the current\
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# y position, along with a small margin
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for index, line in enumerate(text_string_lines):
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# Draw a line of text
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draw_interf.text((x, y), line, font=FONT_USER_INFO, fill=COLOR_TEXT)
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# Increment y to draw the next line at the adequate height
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y += line_height[index] + LINE_MARGIN
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# Load the user photo (read-mode). It should be a 250x250 circle
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#user_photo = Image.open('userprofilepic.png', 'r').convert("RGBA")
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# Paste the user photo into the working image. We also use the photo for\
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# its own mask to keep the photo's transparencies
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#img_final.paste(user_photo, COORD_PHOTO, mask=user_photo)
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# Finally, save the created image
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img_final.save(f'{img_name}.png')
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# -----------------------------------------------------------------------------
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im = Image.open(img_name+".png")
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width_orig, height_orig = im.size
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print(width_orig, height_orig)
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im_bar = Image.open("generation.png")
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width_orig_x, height_orig_x = im_bar.size
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print(width_orig_x, height_orig_x)
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im_bar = im_bar.resize((int(width_orig / 1), int(height_orig / 1)))
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new_im = Image.new('RGB', (2*im.size[0],1*im_bar.size[1]), (250,250,250))
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new_im.paste(im, (0,0))
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new_im.paste(im_bar, (im.size[0],0))
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new_im.save('finalimage.png')
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return 'finalimage.png'
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with gr.Blocks() as demo:
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gr.Markdown("<h1><center>Ad for Your Business</center></h1>")
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gr.Markdown(
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"""ChatGPT based Insights from <a href="https://www.decodem.ai">Decodem.ai</a> for businesses.\nWhile ChatGPT has multiple use cases we have evolved specific use cases/ templates for businesses \n\n This template provides ideas on how a business can generate Advertisement ideas for a product. Enter a product area to size and get the results. Use examples as a guide. We use a equally powerful AI model bigscience/bloom."""
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)
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textbox = gr.Textbox(placeholder="Enter product name...", lines=1,label='Your product')
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btn = gr.Button("Generate")
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#output1 = gr.Textbox(lines=2,label='Market Sizing Framework')
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output_image = gr.components.Image(label="Your Advertisement")
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btn.click(getadvertisement,inputs=[textbox], outputs=[output_image])
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examples = gr.Examples(examples=['spectacles','rice cooker','smart watch','coffee mug',],
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inputs=[textbox])
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