clean up

README.md

@@ -27,7 +27,7 @@ This projects aims to monitor in-demand skills for machine learning roles. Skill
 - **Weights & Biases**: Used for model training monitoring as well as model storing.
 - **OpenAI API**: Used to extract ground-truth from job descriptions by leveraging multi-shot learning and prompt engineering.
 
-
+   
 # High-Level Overview
 
 ## Models

create_sample_skills.py

@@ -1,38 +0,0 @@
-# Generating sample folder structure and files with multiple skills per file
-
-import os
-
-# Base folder for the structure
-base_folder = "tags"
-
-# Sample data: dates and skills for each date
-sample_dates = ["03-01-2024", "04-01-2024", "05-01-2024"]
-sample_skills = {
-    "03-01-2024": [
-        ["Python", "Machine Learning", "Data Analysis"],
-        ["Python", "Deep Learning"],
-        ["Data Science", "AI"]
-    ],
-    "04-01-2024": [
-        ["Python", "AI", "Data Analysis"],
-        ["Deep Learning", "Machine Learning"],
-        ["AI", "Data Engineering"]
-    ],
-    "05-01-2024": [
-        ["AI", "Machine Learning", "Python"],
-        ["Data Science", "Deep Learning"],
-        ["Python", "AI", "Cloud Computing"]
-    ]
-}
-
-# Create the folder structure and files
-for date in sample_dates:
-    date_folder = os.path.join(base_folder, date)
-    os.makedirs(date_folder, exist_ok=True)
-    
-    for i, skills in enumerate(sample_skills[date], start=1):
-        file_path = os.path.join(date_folder, f"{i}.txt")
-        with open(file_path, "w", encoding="utf-8") as f:
-            f.write("\n".join(skills))
-
-print(f"Sample files with multiple skills per file have been generated in the '{base_folder}' folder.")

demo-app.py

@@ -1,6 +0,0 @@
-import plotly.express as px
-import numpy as np
-
-X = np.random.randint(0, 10, (10, 3))
-fig = px.scatter_3d(x=X[:,0], y=X[:, 1], z=X[:, 2])
-fig.show() 

demo.py

@@ -1,39 +0,0 @@
-import gradio as gr
-import plotly.graph_objects as go
-import numpy as np
-
-# Function to create a 3D plot
-def create_3d_plot(x_range, y_range):
-    # Generate 3D data
-    x = np.linspace(-x_range, x_range, 100)
-    y = np.linspace(-y_range, y_range, 100)
-    x, y = np.meshgrid(x, y)
-    z = np.sin(np.sqrt(x**2 + y**2))
-    
-    # Create a 3D surface plot
-    fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])
-    fig.update_layout(
-        scene=dict(
-            xaxis_title='X Axis',
-            yaxis_title='Y Axis',
-            zaxis_title='Z Axis'
-        ),
-        margin=dict(l=0, r=0, b=0, t=0),
-        height=1000    
-    )
-    return fig
-
-# Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("## Interactive 3D Plot with Gradio and Plotly")
-    with gr.Row():
-        x_slider = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="X Range")
-        y_slider = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="Y Range")
-    plot_output = gr.Plot(label="3D Surface Plot")
-    
-    # Update the plot on slider change
-    x_slider.change(create_3d_plot, inputs=[x_slider, y_slider], outputs=plot_output)
-    y_slider.change(create_3d_plot, inputs=[x_slider, y_slider], outputs=plot_output)
-
-# Launch the app
-demo.launch()

few-shot.txt

@@ -1,24 +0,0 @@
-Example #96
-Tokens: ['Public']
-Skill Labels: ['O']
-Knowledge Labels: ['O']
-
-Example #97
-Tokens: ['Technologies']
-Skill Labels: ['O']
-Knowledge Labels: ['O']
-
-Example #98
-Tokens: ['cloud', 'java', 'amazon-web-services']
-Skill Labels: ['O', 'O', 'O']
-Knowledge Labels: ['B', 'B', 'B']
-
-Example #99
-Tokens: ['Job', 'description']
-Skill Labels: ['O', 'O']
-Knowledge Labels: ['O', 'O']
-
-Example #100
-Tokens: ['As', 'a', 'member', 'of', 'our', 'Software', 'Engineering', 'Group', 'we', 'look', 'first', 'and', 'foremost', 'for', 'people', 'who', 'are', 'passionate', 'about', 'solving', 'business', 'problems', 'through', 'innovation', 'and', 'engineering', 'practices', '.']
-Skill Labels: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'I', 'I', 'I', 'I', 'I', 'O']
-Knowledge Labels: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']