Update evtracker.py

evtracker.py

@@ -1,8 +1,7 @@
 import pandas as pd
 import gradio as gr
 
-# 1. The Database (V1 Mock Data for Madurai Market)
-# We can connect this to a live web scraper later.
+# 1. The Database (Hardcoded directly, no CSV needed!)
 ev_data = {
     "Model": ["Bounce Infinity E1", "TVS iQube", "Bajaj Chetak", "Ather 450X", "Ola S1 Pro", "Hero Vida V1"],
     "Price (INR)": [79000, 123000, 122000, 138000, 147000, 145000],
@@ -10,7 +9,7 @@ ev_data = {
     "Top Speed (kmph)": [65, 78, 73, 90, 120, 80],
     "Charging Time (Hrs)": [4.0, 4.5, 4.0, 5.5, 6.5, 6.0]
 }
-df = pd.read_csv("madurai_ev_market.csv")
+df = pd.DataFrame(ev_data)
 
 # 2. The Search Logic
 def recommend_ev(max_budget, min_range):
@@ -20,13 +19,10 @@ def recommend_ev(max_budget, min_range):
     # Sort the results so the cheapest options appear at the top
     filtered_df = filtered_df.sort_values(by="Price (INR)")
     
-    # Return the clean dataframe
     return filtered_df
 
 # 3. The Frontend Architecture
-# We use Gradio Blocks to make it look like a modern, enterprise dashboard
 with gr.Blocks(theme=gr.themes.Soft()) as app:
-    
     gr.Markdown("# 🛵 Smart EV Tracker & Recommender")
     gr.Markdown("Filter the current electric two-wheeler market based on your exact constraints.")
     
@@ -34,7 +30,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
         # Left Column: User Controls
         with gr.Column(scale=1):
             gr.Markdown("### Search Filters")
-            # Defaulting to 60k to match typical entry-level EV searches
             budget_slider = gr.Slider(minimum=50000, maximum=160000, step=5000, value=90000, label="Maximum Budget (₹)")
             range_slider = gr.Slider(minimum=50, maximum=150, step=5, value=75, label="Minimum Range Required (km)")
             search_btn = gr.Button("Find My EV", variant="primary")
@@ -42,11 +37,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
         # Right Column: Data Output
         with gr.Column(scale=2):
             gr.Markdown("### Recommended Models")
-            # Gradio automatically renders Pandas Dataframes as beautiful, interactive tables
             output_table = gr.Dataframe(headers=["Model", "Price (INR)", "Real Range (km)", "Top Speed (kmph)", "Charging Time (Hrs)"])
 
     # Wire the button to the logic function
     search_btn.click(fn=recommend_ev, inputs=[budget_slider, range_slider], outputs=output_table)
 
 # Launch the app
-app.launch()
+if __name__ == "__main__":
+    app.launch()