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Upload 2 files
Browse files- color_palette.py +36 -0
- image.py +88 -0
color_palette.py
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import vertexai
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from vertexai.generative_models import GenerativeModel, Image
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# import streamlit as st
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import os
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PROJECT_ID = "agileai-poc"
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REGION = "us-central1"
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vertexai.init(project=PROJECT_ID, location=REGION)
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# upload_image = st.file_uploader("Upload an image", type=["jpeg","png","jpg"],accept_multiple_files=True)
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# print(upload_image)
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IMAGE_FILE = "/home/lenovo/Pictures/palette_samples/3.jpg"
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image = Image.load_from_file(IMAGE_FILE)
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# if upload_image:
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# image = Image.load_from_file(upload_image)
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generative_multimodal_model = GenerativeModel("gemini-1.0-pro-vision")
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prompt="""" Describe the desired theme,or brand,mood,and palette,detect objects and generate the appealing color palettes on image with respective object and rgb code "
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output format should be "object name :rgb code"
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example:
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tree leaf: (0,255,0)
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pillow :(12,0,230)
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"""
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prompt2="""" Describe the desired theme,or brand,mood,and palette,detect objects with 100 percent accuracy and generate the appealing color palettes on image with respective object ,color name and hexacode "
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output format should be "object name : color :hexacode"
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example:
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object_name :color :(hexacode)
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"""
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# print("\n RGB ************************************** \n")
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# response1 = generative_multimodal_model.generate_content([prompt, image])
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# print(response1.text)
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print("\n hexacode ************************************** \n")
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response2 = generative_multimodal_model.generate_content([prompt2, image])
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print(response2.text)
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image.py
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import vertexai
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from vertexai.generative_models import GenerativeModel, Image
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import streamlit as st
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import os
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# export STREAMLIT_SERVER_MAX_UPLOAD_SIZE=200
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PROJECT_ID = "agileai-poc"
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REGION = "us-central1"
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vertexai.init(project=PROJECT_ID, location=REGION)
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# upload_image = st.file_uploader("Upload an image", type=["jpeg","png","jpg"],accept_multiple_files=True)
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# print(upload_image)
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# IMAGE_FILE = "image-path"
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image =st.file_uploader("upload file",type=["png","jpg","jpeg"],accept_multiple_files=True)
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# if file:
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# file_bytes = file.read()
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# image = Image.load_from_file(file_bytes)
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# if upload_image:
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# image = Image.load_from_file(upload_image)
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generative_multimodal_model = GenerativeModel("gemini-1.0-pro-vision")
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response = generative_multimodal_model.generate_content(["Describe the image", image])
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# prompt="""Here are a few of the things that aren\'t allowed on this chat:
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# 1. Nudity or other sexually suggestive content
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# 2. Hate speech, credible threats or direct attacks on an individual or group
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# 3. Content that contains self-harm or excessive violence
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# 4. Fake or impostor profiles
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# 5. Spam
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# The following behaviour isn\'t allowed on this chat:
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# 1. Posting things that don\'t follow the Community Standards (e.g. threats, hate speech, graphic violence).
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# 2. Using Community to bully, impersonate or harass anyone.
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# User-1: Hold onto your hearts. \"Tiger 3\" will make you laugh, cry, and feel everything in between. In cinemas soon.
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# #love #heartbreak #feelgoodmovie
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# User-2: Loved your look in \"Tiger 3\"! You always look so hot and sexy.
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# User-1: Instead of politicians, let the monkeys govern the countries; at least they will steal only the bananas!
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# User-2: User-2\'s comment is not allowed on this chat because it contains sexually suggestive content.
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# User-1: Loved your look in \"Tiger 3\"! You always look so hot and sexy.
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# User-2: The response was blocked because the input or response may contain descriptions of violence, sexual themes, or otherwise derogatory content. Please try rephrasing your prompt.
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# User-1: Be courageous. Challenge orthodoxy. Stand up for what you believe in. When you are in your rocking chair talking to your grandchildren many years from now, be sure you have a good story to tell
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# User-2:
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# stricly follow prompt analyse {response.text} and declare output as "positive or negative"
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# """
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prompt=""" Analyse the {response.text} and understand the content provided in {response.text}
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whether the content is sarcastic postive or negative etc and
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declare the output as "positive" or "negative"
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"""
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print(response.text)
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result=generative_multimodal_model.generate_content([prompt,response.text])
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print(result.text)
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# import http.client
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# import typing
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# import urllib.request
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# from vertexai.generative_models import GenerativeModel, Image
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# # create helper function
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# def load_image_from_url(image_url: str) -> Image:
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# with urllib.request.urlopen(image_url) as response:
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# response = typing.cast(http.client.HTTPResponse, response)
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# image_bytes = response.read()
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# return Image.from_bytes(image_bytes)
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# # Load images from Cloud Storage URI
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# landmark1 = load_image_from_url(
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# "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
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# )
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# landmark2 = load_image_from_url(
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# "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark2.png"
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# )
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# landmark3 = load_image_from_url(
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# "https://storage.googleapis.com/cloud-samples-data/vertex-ai/llm/prompts/landmark3.png"
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# )
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# # Pass multimodal prompt
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# model = GenerativeModel("gemini-1.0-pro-vision")
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# response = model.generate_content(
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# [
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# landmark1,
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# "city: Rome, Landmark: the Colosseum",
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# landmark2,
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# "city: Beijing, Landmark: Forbidden City",
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# landmark3,
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# ]
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# )
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# print(response)
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