Create app.py

app.py

@@ -0,0 +1,1181 @@
+# Author: Sarvamangala Kokatanur
+
+# ============================================================
+
+# SMART TRAFFIC & EV ANALYTICS SYSTEM
+
+# WITH AGENTIC AI DYNAMIC ANALYTICS DASHBOARD
+
+# ============================================================
+
+import gradio as gr
+import torch
+import numpy as np
+import cv2
+import sqlite3
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from datetime import datetime, timedelta
+from PIL import Image, ImageDraw
+
+from transformers import (
+YolosImageProcessor,
+YolosForObjectDetection
+)
+
+# ============================================================
+
+# LOAD LICENSE PLATE DETECTION MODEL
+
+# ============================================================
+
+processor = YolosImageProcessor.from_pretrained(
+"nickmuchi/yolos-small-finetuned-license-plate-detection"
+)
+
+model = YolosForObjectDetection.from_pretrained(
+"nickmuchi/yolos-small-finetuned-license-plate-detection"
+)
+
+model.eval()
+
+# ============================================================
+
+# DATABASE
+
+# ============================================================
+
+conn = sqlite3.connect(
+"vehicles.db",
+check_same_thread=False
+)
+
+cursor = conn.cursor()
+
+cursor.execute("""
+CREATE TABLE IF NOT EXISTS vehicles(
+
+```
+id INTEGER PRIMARY KEY AUTOINCREMENT,
+
+timestamp TEXT,
+
+license_plate TEXT,
+
+vehicle_status TEXT,
+
+discount INTEGER
+```
+
+)
+""")
+
+conn.commit()
+
+# ============================================================
+
+# PLATE COLOR CLASSIFIER
+
+# ============================================================
+
+def classify_plate_color(plate_img):
+
+```
+img = np.array(plate_img)
+
+# Convert RGB to HSV
+hsv = cv2.cvtColor(
+    img,
+    cv2.COLOR_RGB2HSV
+)
+
+h, w = hsv.shape[:2]
+
+# Inspect left portion of the plate
+left = hsv[:, :int(w * 0.2)]
+
+# Detect green color
+green = cv2.inRange(
+    left,
+    (35, 40, 40),
+    (90, 255, 255)
+)
+
+green_ratio = (
+    np.count_nonzero(green)
+    / green.size
+)
+
+# EV classification
+if green_ratio > 0.15:
+    return "EV"
+
+return "Non-EV"
+```
+
+# ============================================================
+
+# DUPLICATE VEHICLE CHECK
+
+# ============================================================
+
+def is_duplicate_vehicle(plate_number):
+
+```
+cursor.execute("""
+SELECT timestamp
+FROM vehicles
+WHERE license_plate=?
+ORDER BY id DESC
+LIMIT 1
+""", (plate_number,))
+
+row = cursor.fetchone()
+
+if row is None:
+    return False
+
+last_time = datetime.strptime(
+    row[0],
+    "%Y-%m-%d %H:%M:%S"
+)
+
+if datetime.now() - last_time < timedelta(minutes=5):
+    return True
+
+return False
+```
+
+# ============================================================
+
+# SAVE VEHICLE TO DATABASE
+
+# ============================================================
+
+def save_vehicle(plate, status):
+
+```
+if status == "EV":
+    discount = 50
+else:
+    discount = 0
+
+if is_duplicate_vehicle(plate):
+    return "Duplicate"
+
+current_time = datetime.now().strftime(
+    "%Y-%m-%d %H:%M:%S"
+)
+
+cursor.execute("""
+
+INSERT INTO vehicles(
+
+    timestamp,
+    license_plate,
+    vehicle_status,
+    discount
+
+)
+
+VALUES (?, ?, ?, ?)
+
+""", (
+    current_time,
+    plate,
+    status,
+    discount
+))
+
+conn.commit()
+
+return "Saved"
+```
+
+# ============================================================
+
+# GET STATIC DASHBOARD
+
+# ============================================================
+
+def get_dashboard():
+
+```
+df = pd.read_sql(
+    "SELECT * FROM vehicles",
+    conn
+)
+
+fig, axs = plt.subplots(
+    2,
+    2,
+    figsize=(10, 7)
+)
+
+if df.empty:
+
+    for ax in axs.flatten():
+
+        ax.text(
+            0.5,
+            0.5,
+            "No Data Available",
+            ha="center",
+            va="center",
+            fontsize=12
+        )
+
+        ax.axis("off")
+
+    plt.tight_layout()
+
+    return fig
+
+# --------------------------------------------------------
+# EV VS NON-EV
+# --------------------------------------------------------
+
+status_counts = (
+    df["vehicle_status"]
+    .value_counts()
+)
+
+axs[0, 0].bar(
+    status_counts.index,
+    status_counts.values
+)
+
+axs[0, 0].set_title(
+    "EV vs Non-EV Vehicles"
+)
+
+axs[0, 0].set_ylabel(
+    "Number of Vehicles"
+)
+
+
+# --------------------------------------------------------
+# PIE CHART
+# --------------------------------------------------------
+
+axs[0, 1].pie(
+    status_counts.values,
+    labels=status_counts.index,
+    autopct="%1.1f%%"
+)
+
+axs[0, 1].set_title(
+    "Vehicle Distribution"
+)
+
+
+# --------------------------------------------------------
+# DISCOUNT
+# --------------------------------------------------------
+
+total_discount = df["discount"].sum()
+
+axs[1, 0].bar(
+    ["Total Discount"],
+    [total_discount]
+)
+
+axs[1, 0].set_title(
+    "EV Discount Provided"
+)
+
+axs[1, 0].set_ylabel(
+    "Amount (₹)"
+)
+
+
+# --------------------------------------------------------
+# REPORT
+# --------------------------------------------------------
+
+axs[1, 1].axis("off")
+
+ev_count = len(
+    df[
+        df["vehicle_status"] == "EV"
+    ]
+)
+
+non_ev_count = len(
+    df[
+        df["vehicle_status"] == "Non-EV"
+    ]
+)
+
+report = (
+    "TRAFFIC ANALYTICS REPORT\n\n"
+    f"Total Vehicles: {len(df)}\n\n"
+    f"EV Vehicles: {ev_count}\n\n"
+    f"Non-EV Vehicles: {non_ev_count}\n\n"
+    f"Total Discount: ₹{total_discount}"
+)
+
+axs[1, 1].text(
+    0.05,
+    0.95,
+    report,
+    fontsize=11,
+    va="top"
+)
+
+plt.tight_layout()
+
+plt.close(fig)
+
+return fig
+```
+
+# ============================================================
+
+# AGENTIC AI DATABASE TOOLS
+
+# ============================================================
+
+def get_vehicle_dataframe():
+
+```
+df = pd.read_sql(
+    "SELECT * FROM vehicles",
+    conn
+)
+
+return df
+```
+
+def get_total_vehicles():
+
+```
+df = get_vehicle_dataframe()
+
+return len(df)
+```
+
+def get_ev_count():
+
+```
+df = get_vehicle_dataframe()
+
+return len(
+    df[
+        df["vehicle_status"] == "EV"
+    ]
+)
+```
+
+def get_non_ev_count():
+
+```
+df = get_vehicle_dataframe()
+
+return len(
+    df[
+        df["vehicle_status"] == "Non-EV"
+    ]
+)
+```
+
+def get_total_discount():
+
+```
+df = get_vehicle_dataframe()
+
+if df.empty:
+    return 0
+
+return int(df["discount"].sum())
+```
+
+def get_ev_percentage():
+
+```
+total = get_total_vehicles()
+
+ev_count = get_ev_count()
+
+if total == 0:
+    return 0
+
+return round(
+    (ev_count / total) * 100,
+    2
+)
+```
+
+# ============================================================
+
+# DYNAMIC GRAPH GENERATOR
+
+# ============================================================
+
+def generate_ev_graph():
+
+```
+df = get_vehicle_dataframe()
+
+fig, ax = plt.subplots(
+    figsize=(8, 5)
+)
+
+if df.empty:
+
+    ax.text(
+        0.5,
+        0.5,
+        "No Vehicle Data Available",
+        ha="center",
+        va="center",
+        fontsize=14
+    )
+
+    ax.axis("off")
+
+    return fig
+
+counts = (
+    df["vehicle_status"]
+    .value_counts()
+)
+
+ax.bar(
+    counts.index,
+    counts.values
+)
+
+ax.set_title(
+    "EV vs Non-EV Vehicle Analysis"
+)
+
+ax.set_xlabel(
+    "Vehicle Type"
+)
+
+ax.set_ylabel(
+    "Number of Vehicles"
+)
+
+plt.tight_layout()
+
+return fig
+```
+
+def generate_discount_graph():
+
+```
+df = get_vehicle_dataframe()
+
+fig, ax = plt.subplots(
+    figsize=(8, 5)
+)
+
+if df.empty:
+
+    ax.text(
+        0.5,
+        0.5,
+        "No Discount Data Available",
+        ha="center",
+        va="center"
+    )
+
+    ax.axis("off")
+
+    return fig
+
+discount_by_status = (
+    df.groupby("vehicle_status")["discount"]
+    .sum()
+)
+
+ax.bar(
+    discount_by_status.index,
+    discount_by_status.values
+)
+
+ax.set_title(
+    "Discount Analysis"
+)
+
+ax.set_xlabel(
+    "Vehicle Status"
+)
+
+ax.set_ylabel(
+    "Total Discount (₹)"
+)
+
+plt.tight_layout()
+
+return fig
+```
+
+# ============================================================
+
+# AGENTIC AI QUERY ENGINE
+
+# ============================================================
+
+def traffic_ai_agent(user_query):
+
+```
+if not user_query:
+    return (
+        "Please ask a question about the traffic data.",
+        None
+    )
+
+query = user_query.lower()
+
+total = get_total_vehicles()
+ev_count = get_ev_count()
+non_ev_count = get_non_ev_count()
+discount = get_total_discount()
+ev_percentage = get_ev_percentage()
+
+
+# --------------------------------------------------------
+# EV COUNT
+# --------------------------------------------------------
+
+if (
+    "how many ev" in query
+    or "number of ev" in query
+    or "ev count" in query
+):
+
+    response = (
+        f"🤖 AI Analysis:\n\n"
+        f"The system has detected "
+        f"{ev_count} EV vehicle(s).\n\n"
+        f"Total vehicles analysed: {total}."
+    )
+
+    return response, generate_ev_graph()
+
+
+# --------------------------------------------------------
+# NON-EV COUNT
+# --------------------------------------------------------
+
+elif (
+    "non ev" in query
+    or "non-ev" in query
+):
+
+    response = (
+        f"🤖 AI Analysis:\n\n"
+        f"Non-EV vehicles detected: "
+        f"{non_ev_count}.\n\n"
+        f"EV vehicles detected: "
+        f"{ev_count}."
+    )
+
+    return response, generate_ev_graph()
+
+
+# --------------------------------------------------------
+# TOTAL VEHICLES
+# --------------------------------------------------------
+
+elif (
+    "total vehicle" in query
+    or "how many vehicle" in query
+    or "traffic" in query
+):
+
+    response = (
+        f"🤖 AI Traffic Analysis:\n\n"
+        f"Total vehicles detected: "
+        f"{total}.\n\n"
+        f"EV vehicles: {ev_count}\n"
+        f"Non-EV vehicles: {non_ev_count}"
+    )
+
+    return response, generate_ev_graph()
+
+
+# --------------------------------------------------------
+# EV PERCENTAGE
+# --------------------------------------------------------
+
+elif (
+    "percentage" in query
+    or "percent" in query
+):
+
+    response = (
+        f"🤖 AI Analysis:\n\n"
+        f"EV Percentage: "
+        f"{ev_percentage}%\n\n"
+        f"This means {ev_count} out of "
+        f"{total} detected vehicles are EVs."
+    )
+
+    return response, generate_ev_graph()
+
+
+# --------------------------------------------------------
+# DISCOUNT
+# --------------------------------------------------------
+
+elif (
+    "discount" in query
+    or "incentive" in query
+    or "money" in query
+):
+
+    response = (
+        f"🤖 AI Discount Analysis:\n\n"
+        f"Total EV discount provided: "
+        f"₹{discount}.\n\n"
+        f"Number of EV vehicles: "
+        f"{ev_count}."
+    )
+
+    return response, generate_discount_graph()
+
+
+# --------------------------------------------------------
+# CO2 SAVINGS
+# --------------------------------------------------------
+
+elif (
+    "co2" in query
+    or "carbon" in query
+    or "environment" in query
+):
+
+    # Example estimation
+    co2_savings = ev_count * 2.3
+
+    response = (
+        f"🌱 AI Environmental Analysis:\n\n"
+        f"Based on the configured estimation model, "
+        f"{ev_count} EV vehicles could contribute to "
+        f"approximately {co2_savings:.2f} kg of "
+        f"potential CO₂ savings.\n\n"
+        f"Note: This value is an estimated project "
+        f"metric and can be replaced with a validated "
+        f"emission calculation model."
+    )
+
+    return response, generate_ev_graph()
+
+
+# --------------------------------------------------------
+# SUMMARY
+# --------------------------------------------------------
+
+elif (
+    "summary" in query
+    or "report" in query
+    or "overall" in query
+):
+
+    response = (
+        f"🤖 SMART TRAFFIC AI SUMMARY\n\n"
+        f"🚗 Total Vehicles: {total}\n\n"
+        f"⚡ EV Vehicles: {ev_count}\n\n"
+        f"🚙 Non-EV Vehicles: {non_ev_count}\n\n"
+        f"📊 EV Percentage: {ev_percentage}%\n\n"
+        f"💰 Total Discount: ₹{discount}"
+    )
+
+    return response, generate_ev_graph()
+
+
+# --------------------------------------------------------
+# DEFAULT RESPONSE
+# --------------------------------------------------------
+
+else:
+
+    response = (
+        "🤖 I am your Smart Traffic AI Assistant.\n\n"
+        "You can ask questions such as:\n\n"
+        "• How many EV vehicles were detected?\n"
+        "• How many Non-EV vehicles are there?\n"
+        "• What is the total traffic?\n"
+        "• What percentage of vehicles are EVs?\n"
+        "• What is the total EV discount?\n"
+        "• Give me a traffic summary.\n"
+        "• Show CO₂ savings."
+    )
+
+    return response, None
+```
+
+# ============================================================
+
+# MAIN VEHICLE PROCESSING PIPELINE
+
+# ============================================================
+
+def process_image(img):
+
+```
+if img is None:
+
+    return (
+        None,
+        "Please upload an image.",
+        "0",
+        "0",
+        "₹0",
+        get_dashboard()
+    )
+
+
+image = Image.fromarray(img)
+
+
+# --------------------------------------------------------
+# MODEL INPUT
+# --------------------------------------------------------
+
+inputs = processor(
+    images=image,
+    return_tensors="pt"
+)
+
+with torch.no_grad():
+
+    outputs = model(**inputs)
+
+
+target_sizes = torch.tensor([
+    [
+        image.size[1],
+        image.size[0]
+    ]
+])
+
+
+results = processor.post_process_object_detection(
+    outputs,
+    threshold=0.3,
+    target_sizes=target_sizes
+)[0]
+
+
+draw = ImageDraw.Draw(image)
+
+
+ev_count = 0
+non_ev_count = 0
+discount_total = 0
+
+output_text = ""
+
+
+# --------------------------------------------------------
+# NO DETECTION
+# --------------------------------------------------------
+
+if len(results["boxes"]) == 0:
+
+    return (
+        image,
+        "No license plate detected.",
+        "0",
+        "0",
+        "₹0",
+        get_dashboard()
+    )
+
+
+# --------------------------------------------------------
+# PROCESS DETECTED PLATES
+# --------------------------------------------------------
+
+for i, box in enumerate(results["boxes"]):
+
+    x1, y1, x2, y2 = map(
+        int,
+        box.tolist()
+    )
+
+
+    # Crop plate
+    plate = image.crop(
+        (x1, y1, x2, y2)
+    )
+
+
+    # Classify EV / Non-EV
+    status = classify_plate_color(
+        plate
+    )
+
+
+    # Temporary vehicle ID
+    plate_number = (
+        f"Vehicle_"
+        f"{datetime.now().strftime('%H%M%S%f')}"
+        f"_{i}"
+    )
+
+
+    saved = save_vehicle(
+        plate_number,
+        status
+    )
+
+
+    if saved == "Duplicate":
+        continue
+
+
+    # ----------------------------------------------------
+    # EV
+    # ----------------------------------------------------
+
+    if status == "EV":
+
+        ev_count += 1
+
+        discount = 50
+
+        discount_total += discount
+
+        color = "green"
+
+        label = (
+            f"EV | ₹{discount}"
+        )
+
+
+    # ----------------------------------------------------
+    # NON-EV
+    # ----------------------------------------------------
+
+    else:
+
+        non_ev_count += 1
+
+        discount = 0
+
+        color = "red"
+
+        label = "Non-EV"
+
+
+    # Draw bounding box
+
+    draw.rectangle(
+        [x1, y1, x2, y2],
+        outline=color,
+        width=3
+    )
+
+
+    # Draw label
+
+    draw.text(
+        (
+            x1,
+            max(0, y1 - 25)
+        ),
+        label,
+        fill=color
+    )
+
+
+    output_text += (
+
+        f"Vehicle {i+1}\n"
+        f"Plate ID: {plate_number}\n"
+        f"Status: {status}\n"
+        f"Discount: ₹{discount}\n\n"
+
+    )
+
+
+dashboard = get_dashboard()
+
+
+return (
+
+    image,
+
+    output_text,
+
+    str(ev_count),
+
+    str(non_ev_count),
+
+    f"₹{discount_total}",
+
+    dashboard
+
+)
+```
+
+# ============================================================
+
+# GRADIO UI
+
+# ============================================================
+
+css = """
+
+.gradio-container {
+max-width: 98% !important;
+margin: auto;
+}
+
+textarea {
+font-size: 15px !important;
+}
+
+h1 {
+text-align: center;
+}
+
+.block {
+border-radius: 12px;
+}
+
+.gr-image {
+min-height: 350px;
+}
+
+"""
+
+with gr.Blocks(css=css) as demo:
+
+```
+# ========================================================
+# TITLE
+# ========================================================
+
+gr.Markdown(
+    "# 🚦 Smart Traffic & EV Analytics System"
+)
+
+gr.Markdown(
+    "AI License Plate Detection • EV Classification • "
+    "Discount Calculation • Agentic AI Analytics"
+)
+
+
+# ========================================================
+# TAB 1 - VEHICLE DETECTION
+# ========================================================
+
+with gr.Tab("🚗 Vehicle Detection"):
+
+
+    with gr.Row(equal_height=True):
+
+
+        # INPUT
+
+        with gr.Column(scale=1):
+
+            input_img = gr.Image(
+                type="numpy",
+                label="Input Vehicle Image",
+                sources=[
+                    "upload",
+                    "webcam"
+                ],
+                height=350
+            )
+
+
+            btn = gr.Button(
+                "🔍 Scan Vehicle",
+                variant="primary",
+                size="lg"
+            )
+
+
+        # OUTPUT IMAGE
+
+        with gr.Column(scale=1):
+
+            output_img = gr.Image(
+                label="Detection Result",
+                height=350
+            )
+
+
+        # SUMMARY
+
+        with gr.Column(scale=1):
+
+            summary_box = gr.Textbox(
+                label="Detection Summary",
+                lines=16
+            )
+
+
+    # ====================================================
+    # STATISTICS
+    # ====================================================
+
+    with gr.Row():
+
+        ev_box = gr.Textbox(
+            label="⚡ EV Vehicles"
+        )
+
+        non_ev_box = gr.Textbox(
+            label="🚗 Non-EV Vehicles"
+        )
+
+        discount_box = gr.Textbox(
+            label="💰 Total Discount"
+        )
+
+
+    # ====================================================
+    # STATIC DASHBOARD
+    # ====================================================
+
+    gr.Markdown(
+        "## 📊 Traffic Analytics Dashboard"
+    )
+
+    dashboard = gr.Plot(
+        label="Analytics"
+    )
+
+
+    btn.click(
+
+        fn=process_image,
+
+        inputs=input_img,
+
+        outputs=[
+            output_img,
+            summary_box,
+            ev_box,
+            non_ev_box,
+            discount_box,
+            dashboard
+        ]
+
+    )
+
+
+# ========================================================
+# TAB 2 - AGENTIC AI DASHBOARD
+# ========================================================
+
+with gr.Tab("🤖 AI Traffic Assistant"):
+
+
+    gr.Markdown(
+        "## 🤖 Agentic AI Traffic Analytics Dashboard"
+    )
+
+
+    gr.Markdown(
+        "Ask questions about your traffic and EV database "
+        "using natural language."
+    )
+
+
+    user_query = gr.Textbox(
+
+        label="Ask the AI Assistant",
+
+        placeholder=(
+            "Example: How many EV vehicles were detected?"
+        ),
+
+        lines=2
+
+    )
+
+
+    ask_button = gr.Button(
+
+        "🤖 Ask AI",
+
+        variant="primary",
+
+        size="lg"
+
+    )
+
+
+    ai_response = gr.Textbox(
+
+        label="🤖 AI Response",
+
+        lines=12
+
+    )
+
+
+    ai_graph = gr.Plot(
+
+        label="📊 Dynamic AI Generated Graph"
+
+    )
+
+
+    ask_button.click(
+
+        fn=traffic_ai_agent,
+
+        inputs=user_query,
+
+        outputs=[
+            ai_response,
+            ai_graph
+        ]
+
+    )
+
+
+    # ====================================================
+    # SUGGESTED QUESTIONS
+    # ====================================================
+
+    gr.Markdown(
+        """
+```
+
+### 💡 Suggested Questions
+
+• How many EV vehicles were detected?
+
+• How many Non-EV vehicles are there?
+
+��� What is the total traffic?
+
+• What percentage of vehicles are EVs?
+
+• What is the total EV discount?
+
+• Give me an overall traffic summary.
+
+• Show the CO₂ savings.
+"""
+)
+
+# ============================================================
+
+# RUN APPLICATION
+
+# ============================================================
+
+if **name** == "**main**":
+
+```
+demo.queue()
+
+demo.launch(
+    ssr_mode=False
+)
+```