images

README.md

@@ -53,6 +53,10 @@ This projects aims to monitor in-demand skills for machine learning roles. Skill
 
 The follow scripts are scheduled to automate the skill monitoring and model tranining processes continually. 
 
+<div align="center">
+    <img src="./images/in-demand-flow.png" alt="Flow Image">
+</div>
+
 ### 1. Job-posting scraping
 Fetching job descriptions for machine learning from LinkedIn via Rapid API.
 ### 2. Skills tagging with LLM
@@ -63,10 +67,6 @@ The skill extraction model is finetuned with respect to the extracted groundtrut
 Skills are extracted from job-postings with finetuned model
 ### 5. Embedding & visualization
 Extracted skills are embedded, reduced and clustered with an embedding model, UMAP and K-means respectively.
-### 6. Pipeline Flow
-<div align="center">
-    <img src="./images/in-demand-flow.png" alt="Flow Image">
-</div>
 
 
 <h1>

app.py

@@ -98,15 +98,10 @@ fig.update_layout(
 
 with gr.Blocks() as demo:
     
-    # gr.Interface(fn=ner,
-    #     inputs=gr.Textbox(placeholder="Enter sentence here..."),
-    #     outputs=["highlight"],
-    #     examples=examples,
-    #     title="In-demand skills in machine learning (ML) industry"
-    # )
-
+    gr.Markdown("# 3D Visualization of Skills in ML Job Postings", elem_id="title")
     # gr.Markdown("Embedding visualisation of sought skills in ML job posting in Stockholm, Sweden on LinkedIn")
     gr.Plot(fig)
+
     
 
 demo.launch()