Certainly! Here's a detailed set of instructions for the README file:
This Python script utilizes OpenAI's GPT-3 engine to generate personalized study plans for students based on a set of provided parameters. The study plan is designed to address individual academic requirements while aligning with each student's unique needs and aspirations.
Make sure to install the OpenAI Python library. You can use one of the following methods:
-
Install the latest version:
pip install openai --upgrade
-
Install a specific version (e.g., version 0.28):
pip install openai==0.28
-
Run the migration command for version 1.0.0:
openai migrate
-
Set Up Your API Key:
- Replace
'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'
in the code with your actual OpenAI GPT-3 API key.
- Replace
-
Define Student Information:
- Edit the
student_info
dictionary with relevant information about the student, such as name, age, academic level, major subjects, etc.
- Edit the
-
Run the Script:
- Execute the script, and it will generate a personalized study plan based on the provided information.
-
Example Usage:
# Set your OpenAI GPT-3 API key openai.api_key = 'YOUR_OPENAI_API_KEY' # Define student information student_info = { "name": "John Doe", "age": 18, "current_academic_level": "High School", "major_subjects": ["Math", "Science"], "academic_performance": "Transcript data...", "learning_style": "Visual", "extracurricular_activities": ["Chess Club", "Debate Team"], "objectives_and_challenges": "Preparing for SAT", "aspirations": "To become a physicist", "api_key": "YOUR_OPENAI_API_KEY" } # Generate the personalized study plan result = generate_personalized_study_plan(student_info) # Display the generated study plan print(result)
Ensure that you comply with OpenAI's use case policies and guidelines when using the GPT-3 engine. It's recommended to review the OpenAI documentation for the latest information and updates.
/////////////////////////////////////////////////////////////////////////////PDF_SUMMARIZER////////////////////////////////////////////////////////////////////////////////
###INSTRUCTIONS FOR PDF_SUMMARIZER.IPYNB FILE:
Sure, here's a basic README template for your code:
# LangChain Summarizer
## Overview
The LangChain Summarizer is a Python tool designed to summarize the content of books, leveraging natural language processing and machine learning techniques. The code includes functionality to process various book file types, cluster and summarize their content, and generate a cohesive final summary.
## Installation
To use the LangChain Summarizer, you need to install the required dependencies. You can install them using the following commands:
```bash
pip install langchain
pip install python-dotenv
pip install pypdf
pip install openai
pip install tiktoken
Additionally, to use the LangChain Summarizer, you need to obtain an OpenAI API key. Set up a .env
file with your OpenAI API key:
OPENAI_API_KEY=your_actual_api_key
# Import necessary modules
import os
from dotenv import load_dotenv
from langchain_summarizer import generate_summary
# Load environment variables
load_dotenv()
# Provide the OpenAI API key
openai_api_key = os.getenv('OPENAI_API_KEY')
# Example usage with a sample book file
book_path = "/path/to/your/book.pdf"
with open(book_path, 'rb') as uploaded_file:
summary = generate_summary(uploaded_file, openai_api_key, verbose=True)
print(summary)
This function takes a book file, OpenAI API key, and optional parameters to generate a summarized output.
This project is licensed under the MIT License.
Please note that you should replace the placeholder text with the appropriate details for your project. Additionally, make sure to update the license information accordingly.