Added plotly

app.py

@@ -1,6 +1,37 @@
 import pickle as pkl
 import plotly.express as px
 import gradio as gr
+from pydantic import BaseModel, Field
+from typing import List
+
+with open("job_stack_count.pkl", "rb") as f:
+    job_stack_count = pkl.load(f)
+
+job_stack_count['Generative_AI_Frameworks']['Pydantic'] = 1
+job_stack_count['Programming_Languages']['C#'] = 1
+
+class TechStack(BaseModel):
+    GPU_Frameworks: List[str] = Field(..., alias="GPU Frameworks")
+    Programming_Languages: List[str] = Field(..., alias="Programming Languages")
+    Generative_AI_Frameworks: List[str] = Field(..., alias="Generative AI Frameworks")
+    Databases: List[str] = Field(..., alias="Databases")
+    Orchestration_Deployment: List[str] = Field(..., alias="Orchestration & Deployment")
+    APIs_Web_Frameworks: List[str] = Field(..., alias="APIs & Web Frameworks")
+    Big_Data_Technologies: List[str] = Field(..., alias="Big Data Technologies")
+    Cloud_Platforms_Services: List[str] = Field(..., alias="Cloud Platforms & Services")
+    Machine_Learning_Deep_Learning_Libraries: List[str] = Field(..., alias="Machine Learning & Deep Learning Libraries")
+    Data_Visualization_Tools: List[str] = Field(..., alias="Data Visualization Tools")
+    CI_CD_MLOps: List[str] = Field(..., alias="CI/CD & MLOps")
+    Model_Formats_Optimization: List[str] = Field(..., alias="Model Formats & Optimization")
+    Qualifications: List[str] = Field(..., alias="Qualifications")
+    Machine_Learning_AI_Techniques: List[str] = Field(..., alias="Machine Learning & AI Techniques")
+    Machine_Learning_AI_Models: List[str] = Field(..., alias="Machine Learning & AI Models")
+    Tasks_Responsibilities: List[str] = Field(..., alias="Tasks & Responsibilities")
+    Soft_Skills: List[str] = Field(..., alias="Soft Skills")
+    Miscellaneous: List[str] = Field(..., alias="Miscellaneous")
+
+    class Config:
+        populate_by_name = True
 
 # Load the job stack count data
 with open("job_stack_count.pkl", "rb") as f:
@@ -25,22 +56,26 @@ def generate_treemap(category: str):
     
     fig = px.treemap(
         names=stat_dict_keys,
-        parents=[""] * len(stat_dict_keys),  # No parent categories
+        parents=["" for _ in stat_dict_keys],
         values=stat_dict_values,
         hover_name=hover_text
     )
     fig.update_layout(title=category)
     return fig
 
-# Create a Gradio interface
+# Create a Gradio interface in vertical layout
 categories = list(job_stack_count.keys())
-demo = gr.Interface(
-    fn=generate_treemap,
-    inputs=gr.Dropdown(choices=categories, label="Select Category"),
-    outputs=gr.Plot(),
-    title="Tech Stack Treemap Visualization",
-    description="Select a category to visualize the distribution of job tech stacks."
-)
+with gr.Blocks() as demo:
+    gr.Markdown("# Tech Stack Treemap Visualization")
+    gr.Markdown("Select a category to visualize the distribution of job tech stacks.")
+    
+    with gr.Column():  # Ensures vertical layout
+        category_input = gr.Dropdown(choices=categories, label="Select Category")
+        output_plot = gr.Plot()
+    
+    category_input.change(generate_treemap, inputs=category_input, outputs=output_plot)
+
+demo.launch()
 
 # Launch the app
-demo.launch(share=True)
+demo.launch()