Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+import cv2
+import sqlite3
+import pandas as pd
+from PIL import Image, ImageDraw
+from transformers import YolosImageProcessor, YolosForObjectDetection
+import easyocr
+from datetime import datetime
+
+# -------- Database --------
+conn = sqlite3.connect("vehicles.db", check_same_thread=False)
+cursor = conn.cursor()
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS vehicles(
+    plate TEXT,
+    type TEXT,
+    time TEXT
+)
+""")
+conn.commit()
+
+# -------- Models --------
+processor = YolosImageProcessor.from_pretrained("nickmuchi/yolos-small-finetuned-license-plate-detection")
+model = YolosForObjectDetection.from_pretrained("nickmuchi/yolos-small-finetuned-license-plate-detection")
+model.eval()
+reader = easyocr.Reader(['en'])
+
+# -------- Plate Color --------
+def classify_plate_color(plate):
+    img = np.array(plate)
+    hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
+
+    green = np.sum(cv2.inRange(hsv, (35,40,40),(85,255,255)))
+    yellow = np.sum(cv2.inRange(hsv,(15,50,50),(35,255,255)))
+    white = np.sum(cv2.inRange(hsv,(0,0,200),(180,30,255)))
+
+    if green > yellow and green > white:
+        return "EV"
+    elif yellow > green and yellow > white:
+        return "Commercial"
+    else:
+        return "Personal"
+
+# -------- OCR --------
+def read_plate(img):
+    results = reader.readtext(np.array(img))
+    if results:
+        return results[0][1]
+    return "UNKNOWN"
+
+# -------- Dashboard --------
+def get_stats():
+    df = pd.read_sql("SELECT * FROM vehicles", conn)
+    if len(df)==0:
+        return "No vehicles yet"
+    return df['type'].value_counts().to_string()
+
+# -------- Pipeline --------
+def process_image(img):
+    image = Image.fromarray(img)
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    results = processor.post_process_object_detection(
+        outputs, threshold=0.3,
+        target_sizes=torch.tensor([[image.size[1], image.size[0]]])
+    )[0]
+
+    draw = ImageDraw.Draw(image)
+
+    if len(results["boxes"])==0:
+        return image,"No Plate","",get_stats()
+
+    box = results["boxes"][0].tolist()
+    x1,y1,x2,y2 = map(int,box)
+
+    plate = image.crop((x1,y1,x2,y2))
+    plate_text = read_plate(plate)
+    vtype = classify_plate_color(plate)
+
+    cursor.execute("INSERT INTO vehicles VALUES (?,?,?)",
+                   (plate_text,vtype,datetime.now().strftime("%H:%M:%S")))
+    conn.commit()
+
+    draw.rectangle([x1,y1,x2,y2],outline="red",width=3)
+    draw.text((x1,y1-10),f"{plate_text} | {vtype}",fill="red")
+
+    return image, plate_text, vtype, get_stats()
+
+# -------- UI --------
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚗 Smart Traffic & EV Detection System")
+
+    img = gr.Image(type="numpy", sources=["upload","webcam"])
+    out_img = gr.Image()
+    plate = gr.Textbox(label="Number Plate")
+    vtype = gr.Textbox(label="Vehicle Type")
+    stats = gr.Textbox(label="Dashboard")
+
+    btn = gr.Button("Scan Vehicle")
+    btn.click(process_image, img, [out_img,plate,vtype,stats])
+
+demo.launch()