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import gradio as gr
from PIL import Image
import numpy as np
import cv2
from ultralytics import YOLO
from transformers import pipeline
import matplotlib.pyplot as plt
# Load models
yolo_model = YOLO("yolov8n.pt")
damage_pipe = pipeline("image-classification", model="beingamit99/car_damage_detection")
# License Plate Detection
def detect_license_plate(img: Image.Image):
results = yolo_model.predict(img)
result = results[0]
img_array = np.array(img.convert("RGB"))
for box in result.boxes.xyxy:
x1, y1, x2, y2 = map(int, box)
img_array = cv2.rectangle(img_array, (x1, y1), (x2, y2), (0, 255, 0), 2)
return Image.fromarray(img_array)
# Car Damage Classification
def classify_damage(image: Image.Image):
results = damage_pipe(image)
labels = [res["label"] for res in results]
scores = [res["score"] for res in results]
fig, ax = plt.subplots()
ax.barh(labels, scores, color="crimson")
ax.set_xlabel("Confidence")
ax.set_xlim(0, 1)
ax.set_title("Damage Type Classification")
plt.tight_layout()
return fig
# Build UI
with gr.Blocks() as app:
gr.Markdown("# 🚘 Car Analyzer\nChoose a tool below:")
with gr.Tab("🛂 License Plate Detection"):
with gr.Row():
with gr.Column():
lp_input = gr.Image(type="pil", label="Upload Car Image")
lp_btn = gr.Button("Detect")
with gr.Column():
lp_output = gr.Image(label="Detected License Plate")
lp_btn.click(detect_license_plate, inputs=lp_input, outputs=lp_output)
with gr.Tab("💥 Car Damage Classification"):
with gr.Row():
with gr.Column():
dmg_input = gr.Image(type="pil", label="Upload Damaged Car Image")
dmg_btn = gr.Button("Classify")
with gr.Column():
dmg_output = gr.Plot(label="Classification Results")
dmg_btn.click(classify_damage, inputs=dmg_input, outputs=dmg_output)
app.launch()
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