Spaces:

BalajiM
/

evtracker

Sleeping

Upload app.py

d6bb049 verified 7 months ago

2.35 kB

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.
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],
    "Real Range (km)": [85, 100, 90, 105, 143, 110],
    "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.DataFrame(ev_data)

# 2. The Search Logic
def recommend_ev(max_budget, min_range):
    # Filter the dataframe based on user slider inputs
    filtered_df = df[(df["Price (INR)"] <= max_budget) & (df["Real Range (km)"] >= 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.")
    
    with gr.Row():
        # 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")
            
        # 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()

	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.
	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],
	"Real Range (km)": [85, 100, 90, 105, 143, 110],
	"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.DataFrame(ev_data)

	# 2. The Search Logic
	def recommend_ev(max_budget, min_range):
	# Filter the dataframe based on user slider inputs
	filtered_df = df[(df["Price (INR)"] <= max_budget) & (df["Real Range (km)"] >= 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.")

	with gr.Row():
	# 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")

	# 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()