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@ -186,16 +186,16 @@
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limitations under the License.

63
README.md
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@ -2,14 +2,20 @@
This repo is a Python open source library for making a faster scraping using AI and without any knowledge about the HTML code.
The tech stack is fully in Python and the main libraries used are pydantic, langchain and requests.
The tech stack is fully in Python and the main libraries used are pydantic, langchain and requests. All the required libraries are in the requirements.txt file.
The use of this library allows to scrape and extract informations from websites in just few seconds instead of write ad-hoc code for each website.
This library can work passing as a parameter from the code the HTML to scrape or it can work passing the
link of the website that you want to extract informations.
# Setup
# 🔍 Demo
Try out AmazScraper in your browser:
[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/VinciGit00/AmazScraper)
# 🔧 Quick Setup
Follow the following steps:
@ -18,8 +24,8 @@ Follow the following steps:
```
2. (Optional)
```bash
python -m venv venv
source ./venv/bin/activate
python -m venv venv
source ./venv/bin/activate
```
3. ```bash
pip install -r requirements.txt
@ -44,34 +50,40 @@ API_KEY="your openai.com api key"
8. You are ready to go! 🚀
9. Try running the examples using:
```bash
python -m examples.html_scraping
```
or
```bash
python -m AmazScraper.examples.html_scraping
```
# Practical use
```bash
python -m examples.html_scraping
```
## Using AmazScraper as a library
or
```bash
python -m AmazScraper.examples.html_scraping
```
# 📖 Examples
```python
from classes.class_generator import Generator
import os
from dotenv import load_dotenv
from utils.getter import scraper
from utils.class_generator import Generator
from utils.getter import get_function, scraper
load_dotenv()
MY_ENV_VAR = os.getenv('API_KEY')
values = [
{
"title": "title",
"title": "title_website",
"type": "str",
"description": "Title of the items"
"description": "Give me the website name"
}
]
if __name__ == "__main__":
generator_instance = Generator(values, 0, "gpt-3.5-turbo")
generator_instance = Generator(values, MY_ENV_VAR, 0, "gpt-3.5-turbo")
res = generator_instance.invocation(scraper("https://www.mockupworld.co", 4197))
@ -81,8 +93,13 @@ if __name__ == "__main__":
### Case 2: Passing your own HTML code
```python
import sys
from classes.class_generator import Generator
import os
from dotenv import load_dotenv
from utils.class_generator import Generator
load_dotenv()
MY_ENV_VAR = os.getenv('API_KEY')
values = [
{
@ -132,7 +149,7 @@ query_info = '''
if __name__ == "__main__":
generator_instance = Generator(values, 0, "gpt-3.5-turbo")
generator_instance = Generator(values, MY_ENV_VAR, 0, "gpt-3.5-turbo")
res = generator_instance.invocation(query_info)
print(res)
@ -145,13 +162,13 @@ Is it possible to run the examples through the command line inside the principal
For the first example:
```bash
python -m examples.value_scraping
python -m examples.value_scraping
```
For the second example:
```bash
python -m examples.html_scraping
python -m examples.html_scraping
```
# Example of output

11
amazscraper/class_creator.py
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@ -1,14 +1,17 @@
import time
base_script = '''
from langchain_core.pydantic_v1 import BaseModel, Field
class _Response(BaseModel):
'''
# This function creates a class at runtime using the values from the list.
def create_class(data_dict: dict):
'''
This function creates a class at runtime using the values from the list.
'''
for elem in data_dict:
global base_script
base_script = base_script + f" {elem['title']}: {elem['type']} = Field(description='{elem['description']}')\n"
with open("./pydantic_class.py", "w") as f:
f.write(base_script)
with open("./utils/pydantic_class.py", "w") as f:
f.write(base_script)

9
amazscraper/class_generator.py
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@ -1,4 +1,3 @@
import os
from dotenv import load_dotenv
from .pydantic_class import _Response
from .class_creator import create_class
@ -7,12 +6,8 @@ from langchain.prompts import PromptTemplate
from langchain_core.pydantic_v1 import Field
from langchain.output_parsers import PydanticOutputParser
load_dotenv()
MY_ENV_VAR = os.getenv('API_KEY')
class Generator:
def __init__(self, values:list, temperature_param:float = 0, model_name:str = "gpt-3.5-turbo"):
def __init__(self, values:list, api_key:str, temperature_param:float = 0, model_name:str = "gpt-3.5-turbo"):
"""
Initializes the Generator object.
@ -35,7 +30,7 @@ class Generator:
partial_variables={"format_instructions": self.parser.get_format_instructions()},
)
self.model = ChatOpenAI(openai_api_key=MY_ENV_VAR, temperature=temperature_param, model=model_name)
self.model = ChatOpenAI(openai_api_key=api_key, temperature=temperature_param, model=model_name)
self.chain = self.prompt | self.model | self.parser

2
amazscraper/getter.py
View File

@ -15,7 +15,7 @@ def get_function(link:str, param = HEADERS) -> str:
Returns:
str: The content of the response as a string.
"""
response = requests.get(url=link, headers=HEADERS)
response = requests.get(url=link, headers=param)
return str(response.content)
def scraper(link: str, max_char: int) -> str:

2
amazscraper/pydantic_class.py
View File

@ -2,4 +2,4 @@
from langchain_core.pydantic_v1 import BaseModel, Field
class _Response(BaseModel):
title: str = Field(description='Title of the news')
title_website: str = Field(description='Give me the website name')

0
.env.example → examples/.env.example
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9
examples/html_scraping.py
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@ -1,6 +1,11 @@
import sys
import os
from dotenv import load_dotenv
from amazscraper.class_generator import Generator
load_dotenv()
MY_ENV_VAR = os.getenv('API_KEY')
values = [
{
"title": "title",
@ -49,7 +54,7 @@ query_info = '''
if __name__ == "__main__":
generator_instance = Generator(values, 0, "gpt-3.5-turbo")
generator_instance = Generator(values, MY_ENV_VAR, 0, "gpt-3.5-turbo")
res = generator_instance.invocation(query_info)
print(res)

24
examples/value_scraping.py Normal file
View File

@ -0,0 +1,24 @@
import os
from dotenv import load_dotenv
from utils.getter import scraper
from utils.class_generator import Generator
load_dotenv()
MY_ENV_VAR = os.getenv('API_KEY')
values = [
{
"title": "title_website",
"type": "str",
"description": "Give me the website name"
}
]
if __name__ == "__main__":
generator_instance = Generator(values, MY_ENV_VAR, 0, "gpt-3.5-turbo")
res = generator_instance.invocation(scraper("https://www.mockupworld.co", 4197))
print(res)