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OrbiterOrbiter
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more scripts (image processing, wikipedia evaluation)
1 parent 792ed03 commit 12bbf5c

10 files changed

+191
-1
lines changed

‎.gitignore

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@@ -2,3 +2,5 @@
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*/.DS_Store
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context/pg3008.txt
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context/pg50221.txt
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image/images/*.png
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image/images/*.jpg

‎api/chat_python_httpclient.py

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@@ -1,7 +1,6 @@
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import http.client
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import json
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conn = http.client.HTTPConnection("localhost", 11434)
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payload = {
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"model": "llama3.2",
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"temperature": 0.1,
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],
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"stream": False
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}
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conn = http.client.HTTPConnection("localhost", 11434)
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conn.request("POST", "/v1/chat/completions", json.dumps(payload),
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{"Content-Type": "application/json"})
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response_text = conn.getresponse().read().decode()

‎context/wikipedia_news.py

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import difflib
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import mwclient
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from langchain_ollama import ChatOllama
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from datetime import datetime, timedelta
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def wikipedia_news(page_title, weeks):
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page = mwclient.Site('en.wikipedia.org').pages[page_title]
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prev_content, lines = None, []
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for rev in page.revisions(start=datetime.now() - timedelta(weeks=weeks), dir='newer', prop='content'):
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current_content = rev.get('*', '')
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if prev_content:
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diff_lines = difflib.unified_diff(prev_content.splitlines(), current_content.splitlines(), lineterm='')
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lines.extend(line for line in diff_lines if line.startswith('+') and not line.startswith('+++'))
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prev_content = current_content
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return lines
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def analyst(news):
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llm = ChatOllama(model = "gemma3:4b", temperature = 0.1, num_predict = 1024)
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messages = [
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("system", "You are a news assistant and a financial analyst. Summarize and analyse the text from the user input. The user reports company news. " +
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"Make sections about the following topics: what happened, is this good or bad for the company, what is the possible impact on the stock price."),
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("human", news),
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]
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return llm.invoke(messages).content
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companies = ["Tesla, Inc.", "Apple Inc.", "Microsoft Corporation", "Amazon.com, Inc.", "Alphabet Inc.", "Facebook, Inc.",
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"NVIDIA Corporation", "PayPal Holdings, Inc.", "Netflix, Inc.", "Adobe Inc.", "Salesforce.com, Inc.", "Intel Corporation",
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"Cisco Systems, Inc.", "Oracle Corporation", "IBM", "Qualcomm Inc.", "Zoom Video Communications, Inc.", "Spotify Technology S.A.",
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"Snap Inc.", "Twitter, Inc.", "Uber Technologies, Inc.", "Lyft, Inc.", "Airbnb, Inc.", "DoorDash, Inc.",
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"Palantir Technologies Inc.", "Snowflake Inc.", "Roblox Corporation", "Coinbase Global, Inc.", "Unity Software Inc.",
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"C3.ai, Inc.", "UiPath Inc.", "Asana, Inc.", "Slack Technologies, Inc.", "Atlassian Corporation Plc"]
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news = '\n'.join(wikipedia_news("Tesla, Inc.", 1))
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#print(news)
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print(analyst(news))

‎context/wikipedia_stockanalyst.py

Whitespace-only changes.

‎image/image_basic_ollama_api.py

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import base64
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import http.client
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import json
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image_path = "images/scalarproduct.png"
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with open(image_path, "rb") as image_file:
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base64_image = base64.b64encode(image_file.read()).decode('utf-8')
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payload = {
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"model": "gemma3:4b",
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"temperature": 0.1,
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"max_tokens": 1024,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": [
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{"type": "text", "text": "Explain whats in the image: first write down the exact content of the image then explain it."},
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}}
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]}
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],
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"stream": False
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}
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try:
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conn = http.client.HTTPConnection("localhost", 11434)
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conn.request("POST", "/v1/chat/completions", json.dumps(payload),
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{"Content-Type": "application/json"})
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response = conn.getresponse()
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response_text = response.read().decode()
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print("Response text read:" + response_text)
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response_json = json.loads(response_text)
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print(json.dumps(response_json, indent=2))
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except Exception as e:
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print(f"Error: {e}")
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finally:
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conn.close()

‎image/image_basic_openai_api.py

+35
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import base64
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import http.client
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import json
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image_path = "images/scalarproduct.png"
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with open(image_path, "rb") as image_file:
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base64_image = base64.b64encode(image_file.read()).decode('utf-8')
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payload = {
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"model": "gemma3:4b",
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"temperature": 0.1,
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"max_tokens": 1024,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": "Explain whats in the image: first write down the exact content of the image then explain it.",
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"images": [base64_image]
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}
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],
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"stream": False
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}
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try:
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conn = http.client.HTTPConnection("localhost", 11434)
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conn.request("POST", "http://localhost:11434/api/chat", json.dumps(payload),
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{"Content-Type": "application/json"})
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response = conn.getresponse()
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response_text = response.read().decode()
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response_json = json.loads(response_text)
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print(json.dumps(response_json, indent=2))
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except Exception as e:
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print(f"Error: {e}")
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finally:
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conn.close()

‎image/image_catalog.py

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import os
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import json
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import base64
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import http.client
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def image_catalog_entry(image_file):
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with open(image_file, "rb") as image_file:
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base64_image = base64.b64encode(image_file.read()).decode('utf-8')
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format = {
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"title": "Image Catalog Entry",
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"type": "object",
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"properties": {
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"description": { "type": "string" },
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"purpose": { "type": "string" },
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"size": { "type": "string" },
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"price": { "type": "string" }
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},
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"required": [ "description", "purpose", "size", "price" ]
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}
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payload = {
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"model": "gemma3:4b",
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"temperature": 0.1,
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"max_tokens": 1024,
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": "Make a catalog entry of a sales object. Describe the object in the image, its purpose, size, and price and write it as json.",
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"images": [base64_image]
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}
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],
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"stream": False,
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"format": format
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}
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try:
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conn = http.client.HTTPConnection("localhost", 11434)
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conn.request("POST", "http://localhost:11434/api/chat", json.dumps(payload),
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{"Content-Type": "application/json"})
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response = conn.getresponse()
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response_text = response.read().decode()
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response_json = json.loads(response_text)
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print(json.dumps(response_json, indent=2))
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except Exception as e:
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print(f"Error: {e}")
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finally:
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conn.close()
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image_folder = "images"
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image_files = os.listdir(image_folder)
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image_files = [f"{image_folder}/{image_file}" for image_file in image_files]
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for image_file in image_files:
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catalog_entry = image_catalog_entry(image_file)
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print(catalog_entry)

‎image/image_fetch.sh

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#!/bin/bash
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mkdir -p images
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urls=(
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"https://upload.wikimedia.org/wikipedia/commons/e/e7/Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/5/56/USA-Stamp-1973-ZIPCode.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/a/a8/Picture_of_fruit_and_vegetables.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/c/c1/Wikipedia_Logo_as_ASCII_Art_recompress.png"
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"https://upload.wikimedia.org/wikipedia/commons/a/a3/June_odd-eyed-cat.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/f/f8/Wikipedia_editor_hat_w_dog.JPG"
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"https://upload.wikimedia.org/wikipedia/en/b/be/Disloyal_man_with_his_girlfriend_looking_at_another_girl.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/0/06/ElectricBlender.jpg"
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"https://upload.wikimedia.org/wikipedia/en/e/ed/Nyan_cat_250px_frame.PNG"
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"https://upload.wikimedia.org/wikipedia/en/f/fd/Pusheen_the_Cat.png"
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"https://upload.wikimedia.org/wikipedia/commons/d/df/202406111126_IMG_1268.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/6/6f/Mychtar_and_his_Snowdog.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/2/28/Commodore64withdisk.jpg"
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"https://upload.wikimedia.org/wikipedia/commons/0/02/Minecraft_Wiki_2023_textless.png"
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"https://upload.wikimedia.org/wikipedia/en/d/de/Doom_ingame_1.png"
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)
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for url in "${urls[@]}"; do
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filename=$(basename "$url")
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if [ ! -f "images/$filename" ]; then
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wget -P images "$url"
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else
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echo "images/$filename already exists, skipping download."
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fi
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done

‎image/images/.gitkeep

Whitespace-only changes.

‎image/images/scalarproduct.png

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