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import json
import re
import gradio as gr
import pandas as pd
import torch
import torch.nn as nn
from datasets import load_dataset
from PIL import Image, ImageFile
from torchvision import models, transforms
ImageFile.LOAD_TRUNCATED_IMAGES = True
PROJECT_ROOT = Path(__file__).resolve().parent
ARTIFACTS_DIR = PROJECT_ROOT / "artifacts"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EVAL_TRANSFORM = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
CHECKPOINT_PATHS = {
"BaselineCNN": PROJECT_ROOT / "baseline_cnn_best.pt",
"ResNet18": PROJECT_ROOT / "resnet18_best.pt",
"ResNet34": PROJECT_ROOT / "resnet34_best.pt",
"ResNet50": PROJECT_ROOT / "resnet50_best.pt",
"ResNet101": PROJECT_ROOT / "resnet101_best.pt",
"ResNet152": PROJECT_ROOT / "resnet152_best.pt",
}
class BaselineCNN(nn.Module):
def __init__(self, classes: int):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 32, 3, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.AdaptiveAvgPool2d((1, 1)),
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Dropout(0.3),
nn.Linear(256, classes),
)
def forward(self, x):
return self.classifier(self.features(x))
def build_resnet(name: str, classes: int):
if name == "ResNet18":
model = models.resnet18(weights=None)
elif name == "ResNet34":
model = models.resnet34(weights=None)
elif name == "ResNet50":
model = models.resnet50(weights=None)
elif name == "ResNet101":
model = models.resnet101(weights=None)
elif name == "ResNet152":
model = models.resnet152(weights=None)
else:
raise ValueError(f"Unsupported model name: {name}")
model.fc = nn.Linear(model.fc.in_features, classes)
return model
def load_class_names():
class_names_path = ARTIFACTS_DIR / "class_names.json"
if not class_names_path.exists():
raise FileNotFoundError(
"artifacts/class_names.json was not found. Run the notebook first so it can export deployment artifacts."
)
with open(class_names_path, "r", encoding="utf-8") as file:
class_names = json.load(file)
if not class_names or len(class_names) <= 1:
raise ValueError("class_names.json is empty or invalid.")
return class_names
def resolve_best_model_name():
best_model_path = ARTIFACTS_DIR / "best_model_name.txt"
if best_model_path.exists():
name = best_model_path.read_text(encoding="utf-8").strip()
if name in CHECKPOINT_PATHS and CHECKPOINT_PATHS[name].exists():
return name
for candidate in ["ResNet152", "ResNet101", "ResNet50", "ResNet34", "ResNet18", "BaselineCNN"]:
if CHECKPOINT_PATHS[candidate].exists():
return candidate
raise FileNotFoundError("No checkpoint files were found next to app.py.")
def load_model(best_model_name: str, num_classes: int):
if best_model_name == "BaselineCNN":
model = BaselineCNN(num_classes)
else:
model = build_resnet(best_model_name, num_classes)
state_dict = torch.load(CHECKPOINT_PATHS[best_model_name], map_location=DEVICE)
model.load_state_dict(state_dict)
model = model.to(DEVICE)
model.eval()
return model
class_names = load_class_names()
best_model_name = resolve_best_model_name()
model = load_model(best_model_name, len(class_names))
def predict_pil_image(image, top_k=5):
if image is None:
return pd.DataFrame(columns=["Class", "Probability"])
if image.mode != "RGB":
image = image.convert("RGB")
image_tensor = EVAL_TRANSFORM(image).unsqueeze(0).to(DEVICE)
with torch.no_grad():
logits = model(image_tensor)
probabilities = torch.softmax(logits, dim=1).squeeze(0)
top_k = min(top_k, len(class_names))
top_probs, top_indices = torch.topk(probabilities, k=top_k)
rows = []
for idx, prob in zip(top_indices.tolist(), top_probs.tolist()):
rows.append({
"Class": class_names[idx],
"Probability": float(prob),
})
return pd.DataFrame(rows)
LISTING_TABLE = None
def normalize_text(text):
text = "" if text is None else str(text).lower().strip()
text = "".join(ch if ch.isalnum() else " " for ch in text)
return " ".join(text.split())
def load_listing_dataset():
global LISTING_TABLE
if LISTING_TABLE is not None:
return LISTING_TABLE
try:
frame = load_dataset("rebrowser/carguruscom-dataset", "car-listings", split="train").to_pandas()
except Exception:
LISTING_TABLE = pd.DataFrame()
return LISTING_TABLE
keep_columns = [
"year", "make", "model", "trim", "bodyStyle", "price", "mileage",
"transmission", "drivetrain", "fuelType", "dealRatingKey",
"sellerCity", "sellerState", "listingUrl", "description"
]
keep_columns = [column for column in keep_columns if column in frame.columns]
frame = frame[keep_columns].copy()
for column in ["year", "price", "mileage"]:
if column in frame.columns:
frame[column] = pd.to_numeric(frame[column], errors="coerce")
for column in [
"make", "model", "trim", "bodyStyle", "transmission",
"drivetrain", "fuelType", "dealRatingKey",
"sellerCity", "sellerState", "listingUrl", "description"
]:
if column in frame.columns:
frame[column] = frame[column].fillna("").astype(str)
frame["make_norm"] = frame.get("make", pd.Series(index=frame.index, dtype=str)).apply(normalize_text)
frame["model_norm"] = frame.get("model", pd.Series(index=frame.index, dtype=str)).apply(normalize_text)
LISTING_TABLE = frame
return LISTING_TABLE
def parse_predicted_car_label(class_name: str):
frame = load_listing_dataset()
year_match = re.search(r"(19\d{2}|20\d{2})", class_name)
year = int(year_match.group(1)) if year_match else None
if frame.empty:
return {"year": year, "make": None, "model": None, "body_style": None}
label = normalize_text(class_name)
matched_make = None
matched_model = None
body_style = None
makes = sorted(
[value for value in frame["make"].dropna().unique() if str(value).strip()],
key=lambda value: len(str(value)),
reverse=True
)
for value in makes:
if normalize_text(value) in label:
matched_make = value
break
if matched_make is not None:
models_for_make = frame[frame["make"] == matched_make]["model"].dropna().unique().tolist()
models_for_make = sorted(
[value for value in models_for_make if str(value).strip()],
key=lambda value: len(str(value)),
reverse=True
)
for value in models_for_make:
if normalize_text(value) in label:
matched_model = value
break
for value in ["sedan", "coupe", "convertible", "suv", "wagon", "hatchback", "minivan", "van", "pickup", "truck"]:
if value in label:
body_style = value
break
return {"year": year, "make": matched_make, "model": matched_model, "body_style": body_style}
def find_matching_listings(make=None, model=None, year=None, body_style=None, max_results=12):
frame = load_listing_dataset()
if frame.empty:
return pd.DataFrame()
filtered = frame.copy()
if make:
filtered = filtered[filtered["make_norm"] == normalize_text(make)]
if model:
model_norm = normalize_text(model)
exact_match = filtered[filtered["model_norm"] == model_norm]
if len(exact_match) > 0:
filtered = exact_match
else:
filtered = filtered[filtered["model_norm"].str.contains(model_norm, na=False)]
if year is not None and "year" in filtered.columns:
filtered = filtered[filtered["year"].between(year - 1, year + 1, inclusive="both")]
if body_style and "bodyStyle" in filtered.columns:
filtered = filtered[filtered["bodyStyle"].str.contains(body_style, case=False, na=False)]
if len(filtered) == 0 and make:
filtered = frame[frame["make_norm"] == normalize_text(make)].copy()
if len(filtered) == 0:
return filtered
filtered = filtered.copy()
filtered["year_distance"] = 0 if year is None else (filtered["year"] - year).abs()
filtered["deal_rank"] = filtered.get("dealRatingKey", pd.Series("NA", index=filtered.index)).map({
"GREAT_PRICE": 0,
"GOOD_PRICE": 1,
"FAIR_PRICE": 2,
"POOR_PRICE": 3,
"OVERPRICED": 4,
"OUTLIER": 5,
"NA": 6,
}).fillna(6)
filtered = filtered.sort_values(
["year_distance", "deal_rank", "price", "mileage"],
ascending=[True, True, True, True]
)
show_columns = [
column for column in [
"year", "make", "model", "trim", "bodyStyle", "price", "mileage",
"transmission", "drivetrain", "fuelType", "dealRatingKey",
"sellerCity", "sellerState", "listingUrl"
] if column in filtered.columns
]
return filtered[show_columns].head(max_results).reset_index(drop=True)
def build_listing_summary(frame, parsed_car):
if frame is None or len(frame) == 0:
return "No matching marketplace listings were found."
lines = [f"Matched listings: {len(frame)}"]
if parsed_car.get("make"):
lines.append(f"Make: {parsed_car['make']}")
if parsed_car.get("model"):
lines.append(f"Model: {parsed_car['model']}")
if parsed_car.get("year"):
lines.append(f"Target year: {parsed_car['year']}")
if "price" in frame.columns and frame["price"].notna().any():
lines.append(f"Price range: ${int(frame['price'].min()):,} - ${int(frame['price'].max()):,}")
if "mileage" in frame.columns and frame["mileage"].notna().any():
lines.append(f"Mileage range: {int(frame['mileage'].min()):,} - {int(frame['mileage'].max()):,} miles")
return "\n".join(lines)
def format_listing_table(frame):
if frame is None or len(frame) == 0:
return pd.DataFrame(
columns=[
"Year",
"Make",
"Model",
"Trim",
"Body Style",
"Price",
"Mileage",
"Transmission",
"Drivetrain",
"Fuel Type",
"Deal Rating",
"City",
"State",
"Listing URL",
]
)
frame = frame.copy().rename(
columns={
"year": "Year",
"make": "Make",
"model": "Model",
"trim": "Trim",
"bodyStyle": "Body Style",
"price": "Price",
"mileage": "Mileage",
"transmission": "Transmission",
"drivetrain": "Drivetrain",
"fuelType": "Fuel Type",
"dealRatingKey": "Deal Rating",
"sellerCity": "City",
"sellerState": "State",
"listingUrl": "Listing URL",
}
)
if "Price" in frame.columns:
frame["Price"] = frame["Price"].apply(
lambda value: "—" if pd.isna(value) else f"${int(value):,}"
)
if "Mileage" in frame.columns:
frame["Mileage"] = frame["Mileage"].apply(
lambda value: "—" if pd.isna(value) else f"{int(value):,} mi"
)
order = [
"Year",
"Make",
"Model",
"Trim",
"Body Style",
"Price",
"Mileage",
"Transmission",
"Drivetrain",
"Fuel Type",
"Deal Rating",
"City",
"State",
"Listing URL",
]
order = [column for column in order if column in frame.columns]
return frame[order].reset_index(drop=True)
def run_demo(image):
if image is None:
return (
"Please upload a car image.",
pd.DataFrame(),
"Marketplace summary will appear here.",
format_listing_table(pd.DataFrame()),
)
predictions = predict_pil_image(image)
parsed_car = parse_predicted_car_label(predictions.iloc[0]["Class"])
listings = find_matching_listings(
make=parsed_car.get("make"),
model=parsed_car.get("model"),
year=parsed_car.get("year"),
body_style=parsed_car.get("body_style"),
max_results=12,
)
summary = (
f"Best model: {best_model_name}\n"
f"Top prediction: {predictions.iloc[0]['Class']}\n"
f"Confidence: {predictions.iloc[0]['Probability']:.4f}"
)
listing_summary = build_listing_summary(listings, parsed_car)
return summary, predictions, listing_summary, format_listing_table(listings)
simple_css = """
:root {
--sc-accent: #C0504D;
--sc-accent-dark: #A2413F;
--sc-accent-soft: #F6E7E6;
--sc-white: #FFFFFF;
--sc-bg: #F4F5F7;
--sc-surface: #FAFAFB;
--sc-graphite: #2C2C31;
--sc-graphite-2: #3A3A40;
--sc-border: #D9DDE2;
--sc-muted: #6D7278;
--sc-text: #202327;
}
.gradio-container {
background:
linear-gradient(180deg, var(--sc-graphite) 0 118px, var(--sc-bg) 118px 100%);
font-family: Arial, Helvetica, sans-serif !important;
color: var(--sc-text);
}
#page {
max-width: 1220px;
margin: 0 auto;
padding: 24px 18px 40px 18px;
}
#topbar {
display: flex;
align-items: center;
justify-content: space-between;
gap: 24px;
color: white;
margin-bottom: 20px;
}
#brand {
display: flex;
flex-direction: column;
gap: 4px;
}
#brand h1 {
margin: 0;
font-size: 24px;
line-height: 1;
letter-spacing: 0.01em;
font-style: italic;
text-transform: uppercase;
font-weight: 900;
color: white;
}
#brand h1 span {
color: var(--sc-accent);
}
#brand p {
margin: 0;
font-size: 12px;
letter-spacing: 0.04em;
color: #C9CDD2;
text-transform: uppercase;
}
#hero {
background:
linear-gradient(135deg, rgba(255,255,255,0.06), rgba(255,255,255,0.02)),
linear-gradient(180deg, var(--sc-graphite-2), var(--sc-graphite));
color: white;
padding: 22px 26px;
margin-bottom: 18px;
border-left: 4px solid var(--sc-accent);
box-shadow: 0 12px 30px rgba(20, 20, 24, 0.15);
}
#hero p {
margin: 0;
max-width: 860px;
font-size: 15px;
color: #D5D8DC;
line-height: 1.55;
}
.panel {
background: var(--sc-white);
border: 1px solid var(--sc-border);
box-shadow: 0 10px 24px rgba(32, 35, 39, 0.06);
padding: 14px;
}
.section-label {
margin: 0 0 8px 0;
font-size: 12px;
text-transform: uppercase;
letter-spacing: 0.08em;
color: var(--sc-muted);
font-weight: 700;
}
.section-label-tight {
margin: 0 0 4px 0;
font-size: 12px;
text-transform: uppercase;
letter-spacing: 0.08em;
color: var(--sc-muted);
font-weight: 700;
}
.highlight-card {
background: linear-gradient(180deg, #34363B, #25272B);
border: 1px solid #43464D;
color: white;
padding: 12px;
}
.highlight-card textarea,
.highlight-card input {
background: transparent !important;
color: white !important;
}
button.primary {
background: var(--sc-accent) !important;
color: white !important;
border: 1px solid var(--sc-accent-dark) !important;
border-radius: 0 !important;
font-weight: 700 !important;
text-transform: uppercase;
letter-spacing: 0.04em;
box-shadow: none !important;
}
button.primary:hover {
background: #AA4542 !important;
}
.gradio-container button.secondary,
.gradio-container .block,
.gradio-container .gr-box,
.gradio-image,
.gradio-dataframe,
textarea,
input {
border-radius: 0 !important;
}
.gradio-image {
border: 1px solid var(--sc-border) !important;
background: white !important;
}
.gradio-dataframe table thead tr th {
background: var(--sc-graphite) !important;
color: white !important;
border-color: var(--sc-graphite) !important;
font-weight: 700 !important;
}
.gradio-dataframe table tbody tr:nth-child(even) td {
background: var(--sc-surface) !important;
}
.gradio-dataframe table tbody tr td {
border-color: var(--sc-border) !important;
}
.gradio-container .wrap.svelte-1ipelgc,
.gradio-container .contain {
background: transparent !important;
}
#results-grid {
gap: 18px;
align-items: start;
}
.compact-box {
margin-top: 0 !important;
padding-top: 0 !important;
}
@media (max-width: 900px) {
#topbar {
flex-direction: column;
align-items: flex-start;
}
}
"""
with gr.Blocks(css=simple_css) as demo:
with gr.Column(elem_id="page"):
gr.HTML(
"""
<div id="topbar">
<div id="brand">
<h1>Stanford<span>Cars</span></h1>
<p>Image Classification Capstone Project</p>
</div>
</div>
<div id="hero">
<p>
Upload an image from a computer and get a prediction view with model confidence and matched marketplace listings.
</p>
</div>
"""
)
with gr.Row(elem_id="results-grid"):
with gr.Column(scale=5):
with gr.Column(elem_classes=["panel", "highlight-card"]):
gr.HTML('<div class="section-label" style="color:#CFD3D8;">Image Upload</div>')
image_input = gr.Image(
type="pil",
sources=["upload"],
label="Upload a car image from your computer",
height=360,
)
predict_button = gr.Button("Predict", variant="primary")
with gr.Column(scale=7):
with gr.Column(elem_classes=["panel", "highlight-card"]):
gr.HTML('<div class="section-label" style="color:#CFD3D8;">Prediction Summary</div>')
summary_output = gr.Textbox(label="", show_label=False, lines=3)
with gr.Column(elem_classes=["panel", "highlight-card"]):
gr.HTML('<div class="section-label" style="color:#CFD3D8;">Marketplace Summary</div>')
listing_summary_output = gr.Textbox(label="", show_label=False, lines=5)
with gr.Column(elem_classes=["panel", "highlight-card"]):
gr.HTML('<div class="section-label-tight" style="color:#CFD3D8;">Top Predictions</div>')
predictions_output = gr.Dataframe(label="", show_label=False, interactive=False)
with gr.Column(elem_classes=["panel", "highlight-card"]):
gr.HTML('<div class="section-label" style="color:#CFD3D8;">Matched Marketplace Listings</div>')
listings_output = gr.Dataframe(
label="",
show_label=False,
interactive=False,
wrap=True,
)
predict_button.click(
fn=run_demo,
inputs=image_input,
outputs=[
summary_output,
predictions_output,
listing_summary_output,
listings_output,
],
)
if __name__ == "__main__":
demo.launch() |