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
+import matplotlib.pyplot as plt
+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'], gpu=False)
+
+# -------------------- Plate Color Classifier --------------------
+def classify_plate_color(plate_img):
+    img = np.array(plate_img)
+    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(plate_img):
+    results = reader.readtext(np.array(plate_img))
+    if results:
+        return results[0][1]
+    return "UNKNOWN"
+
+# -------------------- Dashboard --------------------
+def get_dashboard():
+    df = pd.read_sql("SELECT * FROM vehicles", conn)
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+
+    if len(df) == 0:
+        ax.text(0.5, 0.5, "No vehicles scanned yet",
+                ha="center", va="center", fontsize=14)
+        ax.axis("off")
+        return fig
+
+    counts = df["type"].value_counts()
+    counts.plot(kind="bar", ax=ax)
+
+    ax.set_title("Vehicle Classification Dashboard")
+    ax.set_xlabel("Vehicle Type")
+    ax.set_ylabel("Count")
+    ax.grid(axis="y")
+
+    return fig
+
+# -------------------- Main 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 detected", "", get_dashboard()
+
+    x1, y1, x2, y2 = map(int, results["boxes"][0].tolist())
+    plate_img = image.crop((x1, y1, x2, y2))
+
+    plate_text = read_plate(plate_img)
+    vehicle_type = classify_plate_color(plate_img)
+
+    cursor.execute(
+        "INSERT INTO vehicles VALUES (?, ?, ?)",
+        (plate_text, vehicle_type, datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
+    )
+    conn.commit()
+
+    draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
+    draw.text((x1, y1 - 12), f"{plate_text} | {vehicle_type}", fill="red")
+
+    return image, plate_text, vehicle_type, get_dashboard()
+
+# -------------------- Gradio UI --------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## 🚦 Smart Traffic & EV Analytics System")
+    gr.Markdown(
+        "Detects license plates, reads number plate text, "
+        "classifies EV / Commercial / Personal vehicles, "
+        "and shows live analytics."
+    )
+
+    with gr.Row():
+        input_img = gr.Image(type="numpy", sources=["upload", "webcam"])
+        output_img = gr.Image()
+
+    with gr.Row():
+        plate_box = gr.Textbox(label="Number Plate")
+        type_box = gr.Textbox(label="Vehicle Type")
+
+    dashboard = gr.Plot(label="Live Vehicle Dashboard")
+
+    btn = gr.Button("Scan Vehicle")
+    btn.click(
+        process_image,
+        input_img,
+        [output_img, plate_box, type_box, dashboard]
+    )
+
+demo.launch()