| import gradio as gr |
| import pandas as pd |
| import joblib |
| import os |
|
|
| os.environ["MPLCONFIGDIR"] = "/tmp" |
| reg_model = joblib.load("best_vehicle_mpg_model.pkl") |
|
|
| makes = ['FORD', 'TOYOTA', 'HONDA', 'BMW', 'AUDI'] |
| transmissions = ['Automatic', 'Manual', 'CVT', 'Auto-Manual', 'Direct Drive'] |
| fuels = ['Regular Gasoline', 'Premium Gasoline', 'Diesel', 'E85', 'CNG'] |
| classes = ['SUV', 'Compact Cars', 'Mid-size Cars', 'Large Cars', 'Pickup Trucks', 'Station Wagons'] |
|
|
| def predict_mpg(year, cylinders, displ, make, trany, fuel, vclass): |
| input_data = pd.DataFrame([{ |
| "year": year, |
| "cylinders": cylinders, |
| "displ": displ, |
| "make": make, |
| "trany": trany, |
| "fuelType": fuel, |
| "VClass": vclass |
| }]) |
| prediction = reg_model.predict(input_data)[0] |
| return f"🚗 Predicted Combined MPG: {prediction:.2f} MPG" |
|
|
| profile_cards = """ |
| <div style="display: flex; justify-content: center; gap: 40px; flex-wrap: wrap;"> |
| <div class="profile-card"> |
| <img src="https://huggingface.co/spaces/Aaron006/Aaron/resolve/main/Picsart_25-04-15_20-22-25-802.jpg" class="avatar" /> |
| <h2 class="name">Aaron</h2> |
| <p class="title">Machine Learning Explorer</p> |
| <a href="https://www.linkedin.com/in/aaron-alex-mathew/" target="_blank" class="linkedin-btn">💼 Connect on LinkedIn</a> |
| </div> |
| <div class="profile-card"> |
| <img src="https://huggingface.co/spaces/Aaron006/Aaron/resolve/main/1723800318684.jpg" class="avatar" /> |
| <h2 class="name">Agnivesh P A</h2> |
| <p class="title">Machine Learning Explorer</p> |
| <a href="https://www.linkedin.com/in/agnivesh-p-a-4747a1258/" target="_blank" class="linkedin-btn">💼 Connect on LinkedIn</a> |
| </div> |
| </div> |
| """ |
|
|
| built_with = """ |
| <div class="built-with"> |
| <h3>🔧 Built With:</h3> |
| <div class="badges"> |
| <span class="badge">Python</span> |
| <span class="badge">scikit-learn</span> |
| <span class="badge">Gradio</span> |
| </div> |
| </div> |
| """ |
|
|
| custom_css = """ |
| body { background: #0f0f1a; font-family: sans-serif; } |
| |
| .profile-card { |
| width: 280px; |
| height: 380px; |
| text-align: center; |
| padding: 20px; |
| margin: 20px; |
| border-radius: 20px; |
| background: linear-gradient(145deg, #2c2f4a, #1b1c30); |
| box-shadow: 0 0 30px rgba(0,255,255,0.2); |
| transition: transform 0.3s ease-in-out; |
| display: flex; |
| flex-direction: column; |
| justify-content: space-between; |
| } |
| |
| .profile-card:hover { |
| transform: scale(1.05); |
| box-shadow: 0 0 40px rgba(0,255,255,0.4); |
| } |
| |
| .avatar { |
| width: 120px; |
| height: 120px; |
| object-fit: cover; |
| border-radius: 50%; |
| border: 3px solid rgba(255,255,255,0.2); |
| margin-bottom: 10px; |
| align-self: center; |
| } |
| |
| .name { font-size: 22px; color: #ffffff; margin: 6px 0; } |
| .title { font-size: 14px; color: #ccc; margin-bottom: 16px; } |
| |
| .linkedin-btn { |
| padding: 10px 16px; |
| background-color: #0077b5; |
| color: white; |
| border-radius: 8px; |
| text-decoration: none; |
| font-weight: bold; |
| } |
| .linkedin-btn:hover { background-color: #005580; } |
| |
| .built-with { |
| text-align: center; |
| margin-top: 10px; |
| margin-bottom: 30px; |
| color: #ccc; |
| } |
| |
| .built-with h3 { |
| font-size: 18px; |
| color: #fff; |
| margin-bottom: 10px; |
| } |
| |
| .badges { |
| display: flex; |
| justify-content: center; |
| gap: 12px; |
| flex-wrap: wrap; |
| } |
| |
| .badge { |
| padding: 6px 12px; |
| background-color: rgba(255, 255, 255, 0.1); |
| border: 1px solid rgba(255, 255, 255, 0.2); |
| border-radius: 6px; |
| font-size: 13px; |
| color: #fff; |
| } |
| """ |
|
|
| with gr.Blocks(css=custom_css) as demo: |
| gr.HTML(profile_cards) |
| gr.HTML(built_with) |
|
|
| gr.Markdown("## 🚘 Vehicle MPG Predictor") |
| gr.Markdown("Enter vehicle details to predict Combined MPG") |
|
|
| with gr.Row(): |
| with gr.Column(): |
| year = gr.Slider(2000, 2025, value=2020, step=1, label="Year") |
| cylinders = gr.Slider(2, 16, value=4, step=1, label="Cylinders") |
| displ = gr.Slider(1.0, 8.0, value=2.0, step=0.1, label="Engine Displacement (L)") |
| trany = gr.Dropdown(transmissions, label="Transmission") |
| with gr.Column(): |
| make = gr.Dropdown(makes, label="Make") |
| fuel = gr.Dropdown(fuels, label="Fuel Type") |
| vclass = gr.Dropdown(classes, label="Vehicle Class") |
|
|
| result = gr.Textbox(label="Prediction Result") |
| predict_btn = gr.Button("🚀 Predict MPG") |
|
|
| predict_btn.click( |
| fn=predict_mpg, |
| inputs=[year, cylinders, displ, make, trany, fuel, vclass], |
| outputs=result |
| ) |
|
|
| demo.launch() |
