fetchx 1.0.14
fetchx library and its two main functions "call" and "fetch" are designed to allow users to rapidly scrape/automate web applications.
Requires Python: >=3.13

# fetchx
the ultimate Python library for seamless HTTP/HTTPS requests, inspired by the elegance of httpx and the simplicity of browser-native JavaScript **fetch**.
Designed for developers who value speed, clarity, and compatibility, **fetchx** brings you a clean, intuitive API with powerful functions: fetch and afetch.
Whether you're working synchronously or asynchronously, **fetchx** makes your HTTP workflows effortless. Best of all, it's fully compatible with the fetch-style
code generated by Chrome's “Copy as fetch” feature and it is based on HTTP 2.0 protocol.

![Usage](https://raw.githubusercontent.com/jaromirsivic/uuRest/refs/heads/main/logo.png)

## Installation Windows (one-liner)
1. Create an emtpy directory and name it for example "fetchxTest"
2. Press the following two keys on the keyboard WIN + R, type "cmd" and navigate to the new directory using "cd C:\...\fetchxTest\"
3. Type the following command and execute it: 
```bash
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" && set Path=C:\Users\User\.local\bin;%Path% && uv init && uv add fetchx && uv sync --upgrade && uv run python -c "import fetchx.init"
```
4. Double click on win_02_jupyter.bat to see more examples how to use fetchx.
## Installation Windows (using init.bat)
1. Create an emtpy directory and name it for example "fetchxTest"
2. Create an empty file in the newly created "fetchxTest" directory. Name it "win_01_init.bat".
3. Copy the following text into the file "win_01_init.bat":
```bash
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
set Path=C:\Users\User\.local\bin;%Path% 
uv init 
uv add fetchx
uv sync --upgrade
uv lock --upgrade
uv run python -c "import fetchx.init"
pause
```
4. Execute a file by double clicking on it
5. Double click on win_02_jupyter.bat to see more examples how to use fetchx.

## Installation Linux, macOS
1. Create an emtpy directory and name it for example "fetchxTest"
2. Open newly created directory
3. Run the following commands:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "or use wget if there is a problem with curl"
wget -qO- https://astral.sh/uv/install.sh | sh
uv init 
uv add fetchx
uv run python -c "import fetchx.init"
```
4. Run lin_02_jupyter.sh to see more examples how to use fetchx.

## How to use the fetchx

Downloading html source from the server

```python
# first we need to import the library
from fetchx import *

# get a link to the fetch setup object and print the setup
setup = fetch_setup()
print(setup)

# Try to download html from the example.com server
response = fetch("http://example.com")

# print html text
print(response.text)
```

Now lets donwnload a Google logo in SVG and GIF format and save it to the directory:

```python
# first we need to import the library
from fetchx import *

# lets download svg file from wikipedia
response = fetch("https://upload.wikimedia.org/wikipedia/commons/2/2f/Google_2015_logo.svg")

# We can inspect the response status in a greater detail
print(f'response.http_status_code = {response.status_code}')
print(f'response.content_type = {response.content_type}')
print(f'response.data_type = "{response.data_type}"')
print(f'response.content = {response.content}')

# we can save response as a text
response.save_text("./google.svg")
# or use it directly via "text" property
svg = response.text

# lets download the logo in gif format
response = fetch("https://upload.wikimedia.org/wikipedia/commons/b/b9/First-google-logo.gif")
# and save it to google.gif
response.save_binary("./google.gif")
# we can use binary data directly using "binary" property
gif = response.binary
```

Now lets use the fetch function to play with REST API:

```python
# first we need to import the library
from fetchx import *

# get a link to the fetch setup object and print the setup
setup = fetch_setup()

# lets modify the fetch behavior
# we will set recommended minimal timeout to 10 seconds which should be enough
setup["timeout"] = 10
# set verbose_level to 1. We want the fetch/call to be less descriptive
setup["verbose_level"] = 1
# lets check if the default fetch setup was modified properly
print(fetch_setup())

# Lets download a list of objects from https://api.restful-api.dev/objects
response = fetch("https://api.restful-api.dev/objects", {
    "headers": {
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
        "cache-control": "no-cache",
        "pragma": "no-cache",
        "priority": "u=0, i",
        "sec-fetch-user": "?1",
        "upgrade-insecure-requests": "1"
    },
    "body": null,
    "method": "GET",
    "mode": "cors",
    "credentials": "omit"
})

# and print the very first object:
print(response.json[0])
#{
#    "id": "1",
#    "name": "Google Pixel 6 Pro",
#    "data": {
#        "color": "Cloudy White",
#        "capacity": "128 GB"
#    }
#}

# we can parse json into the json object
json_object = response.json.parse()
print(json_object[0].name)
print(json_object[0].data.color)

# we can translate fetch to the call method which is more intuitive and uses more pythonic approach
t = translate_fetch("https://api.restful-api.dev/objects", {
    "headers": {
        "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
        "cache-control": "no-cache",
        "pragma": "no-cache",
        "priority": "u=0, i",
        "sec-fetch-user": "?1",
        "upgrade-insecure-requests": "1"
    },
    "body": null,
    "method": "GET",
    "mode": "cors",
    "credentials": "omit"
})
print(t)

# We can use the call function to call REST API using POST method to create a new object
# This can be done using fetch function as well, but call is much more clean
url = "https://api.restful-api.dev/objects"
method = "POST"
body = {
    "name": "Apple MacBook Pro 16",
    "data": {
        "year": 2019,
        "price": 1849.99,
        "CPU model": "Intel Core i9",
        "Hard disk size": "1 TB"
    }
}
new_object = call(url, method, body)

# as soon as the new object is created we can update it using PUT method
url = f"https://api.restful-api.dev/objects/{new_object['id']}"
method = "PUT"
body = {
    "name": "Apple MacBook Pro 16",
    "data": {
        "year": 2019,
        "price": 2049.99,
        "CPU model": "Intel Core i9",
        "Hard disk size": "1 TB",
        "color": "silver"
    }
}
new_object = call(url, method, body)

# we can update the object using PATCH method if available
url = f"https://api.restful-api.dev/objects/{new_object['id']}"
method = "PATCH"
body = {
    "name": "Apple MacBook Pro 16 (Updated Name)"
}
new_object = call(url, method, body)

# and finaly we can delete the object using DELTE method
url = f"https://api.restful-api.dev/objects/{new_object['id']}"
method = "DELETE"
response = call(url, method)
```

To work with fetch and call asynchronously you can use afetch and acall:
```python
# first we need to import the library
import asyncio
from fetchx import *

response = await afetch(...)
response = await acall(...)
```

For example if there is a need to download several files at once or call several different REST API endpoints in parallel:
```python
import asyncio
from fetchx import *

# create download tasks
tasks = []
for i in range(0, 10):
    custom_setup = fetch_setup(duplicate=True)
    custom_setup["verbose_level"] = 2
    custom_setup["thread"] = i
    tasks.append(
        afetch(
            "https://upload.wikimedia.org/wikipedia/commons/b/b9/First-google-logo.gif",
            setup=custom_setup
        )
    )

# run download tasks in parallel
await asyncio.gather(*tasks)
```

Default behavior of the fetch_setup():
```python
# first we need to import the library
from fetchx import *

# get a link to the fetch setup object
setup = fetch_setup()
print(setup)

# lets modify the fetch and call default behavior
# we will set recommended minimal timeout to 10 seconds which should be enough
setup["timeout"] = 10
# in case of http errors please do not throw errors and try to handle it
# possible values:
#    "fail_fast" - raises exception whenever an error is encountered
#    "try_to_compensate" - hides an error. The property "data_type" will be set to an error.
setup["fail_strategy"] = "try_to_compensate"
# set verbose_level to max. We want the fetch/call to explain everything what it is doing
# in the debug mode otherwise we want the fetch/call to be silent
setup["verbose_level"] = 3 if __debug__ else 0
# add a new default http header which will be sent with every request
setup["headers"].update({"X-My-Custom-Header": "value"})
# thread id is useful when using fetch/call in multi-threaded applications
setup["thread"] = 0
# http version is 2.0 by default. It can be switched back to 1.1 if needed
setup["http_version"] = "2.0"

# lets check if default fetch setup was modified properly
print(fetch_setup())
```