Upload app.py

app.py

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+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()