5 AI Projects you can Build this Weekend

Building projects not only solidifies theoretical knowledge but also enhances problem-solving skills. In this article, we will explore five engaging AI projects that can be built over this weekend. Each project is designed to address real-world problems, allowing you to apply your programming skills effectively using Python.

The following projects all take this problem-first approach. You can take these ideas and implement them directly or (even better) use them as inspiration for solving a problem that you are personally facing. So, without any further ado, let’s quickly get building.

1) Resume Optimizer (Beginner)

One of the most labor-intensive tasks when applying for jobs is tailoring your resume for different positions. Automating this process can save time and ensure that your resume aligns with job descriptions effectively.

Implementation Steps

1. Create a Markdown Resume: Start by formatting your resume in Markdown.

2. Define Prompt Templates: Experiment with different prompts that take your Markdown resume and a job description as inputs.

3. Use OpenAI's API: Implement the automation using OpenAI’s Python API to dynamically rewrite your resume.

4. Convert Formats: Use libraries like markdown and pdfkit to convert the final output into HTML and PDF formats.

Code Snippet
Here's a basic example of how you can use the OpenAI API to modify your resume:

import openai

openai.api_key = "your_sk"

# Define the prompt
prompt = f"""
I have a resume formatted in Markdown and a job description. \
Please adapt my resume to better align with the job requirements while \
maintaining a professional tone...
"""

# Make API call
response = openai.ChatCompletion.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ],
    temperature=0.25
)

# Extract response
resume = response['choices'][0]['message']['content']

2) YouTube Lecture Summarizer (Beginner)

Project Overview
With countless technical talks available online, it can be challenging to keep up with them all. This project aims to create a tool that watches YouTube videos and generates concise summaries.Implementation Steps

1. Extract Video ID: Use regex to extract the video ID from the URL.

2. Retrieve Transcript: Utilize the youtube-transcript-api library to get the transcript of the video.

3. Summarize Content: Feed the transcript into OpenAI’s API for summarization.

Code Snippet
Here’s how you can extract the video ID:

import re

youtube_url = "video link here"
video_id_regex = r'(?:v=|\/)([0-9A-Za-z_-]{11}).*'
match = re.search(video_id_regex, youtube_url)

if match:
    video_id = match.group(1)
else:
    video_id = None

3) Automatically Organizing PDFs (Intermediate)

Project Overview
Managing numerous research papers can be overwhelming. This project focuses on organizing PDFs based on their content using machine learning techniques.Implementation Steps

1. Read PDF Content: Use PyMuPDF to read abstracts from each research paper.

2. Generate Text Embeddings: Utilize sentence-transformers to create vector representations of abstracts.

3. Cluster Similar Papers: Apply clustering algorithms like K-Means from sklearn to group similar documents.

Code Snippet
Here’s how you can calculate embeddings:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")
abstract_list = ["abstract 1", "abstract 2"]
embeddings = model.encode(abstract_list)

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4) Multimodal Search (Intermediate)

Project Overview
Searching through technical reports often involves visual data presented in figures or plots. This project aims to create a multimodal search system that incorporates both text and images.Implementation Steps

1. Extract Text and Images from PDFs: Use PyMuPDF for text extraction and image retrieval.

2. Embed Text and Images Together: Utilize a multimodal embedding model for representation.

3. Compute Similarity Scores: Implement cosine similarity calculations between user queries and embedded data.

Code Snippet
Here’s how you can chunk text from PDFs:

import fitz  # PyMuPDF

def extract_text_chunks(pdf_path, chunk_size, overlap_size):
    pdf_document = fitz.open(pdf_path)
    chunks = []
    
    for page_num in range(len(pdf_document)):
        page = pdf_document[page_num]
        page_text = page.get_text()
        start = 0
        
        while start < len(page_text):
            end = start + chunk_size
            chunk = page_text[start:end]
            chunks.append((page_num + 1, chunk))
            start += chunk_size - overlap_size
    
    return chunks

5) Knowledge Base QA (Advanced)

Project Overview
Creating a question-answering system based on a knowledge base is increasingly sought after in various sectors. This project combines previous elements into an effective QA tool.Implementation Steps

1. Search Knowledge Base: Perform searches over the organized documents.

2. Combine Results with User Queries: Integrate user input with top search results.

3. Create User Interface with Gradio: Develop an interactive interface for users.

Code Snippet
Here’s an example of setting up a Gradio interface:

import gradio as gr

def generate_response(message, history):
    # Your code for generating a response
    return response

demo = gr.ChatInterface(
    fn=generate_response,
    examples=[{"text": "Hello", "files": []}],
    title="Echo Bot",
    multimodal=True
)

demo.launch()

Conclusion

These five projects not only provide an excellent opportunity to apply your Python skills but also address real-world challenges effectively using AI technologies. By focusing on practical applications, you can enhance both your coding abilities and problem-solving skills while creating tools that have tangible benefits in various domains. Embrace these challenges, experiment boldly, and enjoy the learning journey!