app.py

import gradio as gr
import cv2, mediapipe as mp
import numpy as np
import pandas as pd
import sqlite3, geocoder, folium, threading, time
from datetime import datetime
from sklearn.linear_model import LinearRegression
import speech_recognition as sr
import matplotlib.pyplot as plt
from io import BytesIO
import base64

# =========================
# DATABASE
# =========================
conn = sqlite3.connect("emergency.db", check_same_thread=False)
cur = conn.cursor()
cur.execute("""
CREATE TABLE IF NOT EXISTS emergency (
    time TEXT,
    trigger TEXT,
    type TEXT,
    severity INT,
    location TEXT,
    status TEXT
)
""")
conn.commit()

# =========================
# ML SEVERITY MODEL
# =========================
X = np.array([[0,0,0],[1,0,0],[0,1,0],[0,0,1],[1,1,1]])
y = np.array([1,4,5,6,9])
model = LinearRegression()
model.fit(X, y)

# =========================
# AI MODELS
# =========================
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(False,1,0.7)
mp_draw = mp.solutions.drawing_utils
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

VOICE_SOS = {"flag": False}
WOMEN_MODE = {"on": False}

# =========================
# VOICE LISTENER THREAD
# =========================
def voice_listener():
    r = sr.Recognizer()
    mic = sr.Microphone()
    while True:
        try:
            with mic as source:
                audio = r.listen(source, phrase_time_limit=3)
            text = r.recognize_google(audio).lower()
            if any(k in text for k in ["help","sos","emergency","save me"]):
                VOICE_SOS["flag"] = True
        except:
            pass
        time.sleep(0.2)

threading.Thread(target=voice_listener, daemon=True).start()

# =========================
# LOCATION
# =========================
def get_location():
    g = geocoder.ip("me")
    if g.ok:
        return g.latlng, f"{g.city}, {g.state}, {g.country}"
    return [0,0], "Unknown"

# =========================
# MAP
# =========================
def make_map(df):
    if df.empty:
        m = folium.Map(location=[20,78], zoom_start=5)
        return m._repr_html_()
    m = folium.Map(location=[df.iloc[-1]['lat'], df.iloc[-1]['lon']], zoom_start=12)
    for _, row in df.iterrows():
        color = "red" if row['severity']>5 else "orange"
        folium.Marker([row['lat'], row['lon']], 
                      popup=f"{row['type']} | Severity: {row['severity']}", 
                      icon=folium.Icon(color=color)).add_to(m)
    return m._repr_html_()

# =========================
# PROCESS FRAME
# =========================
def process(frame):
    gesture, voice, face = 0,0,0
    status = "Monitoring..."

    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    res = hands.process(img)

    # Hand gesture detection
    if res.multi_hand_landmarks:
        for lm in res.multi_hand_landmarks:
            mp_draw.draw_landmarks(frame, lm, mp_hands.HAND_CONNECTIONS)
            if lm.landmark[8].y < lm.landmark[6].y:
                gesture = 1
                status = "✋ SOS Gesture"
            if lm.landmark[8].y > lm.landmark[6].y:
                gesture = 1
                status = "✊ Threat Gesture"

    # Voice SOS
    if VOICE_SOS["flag"]:
        voice = 1
        status = "🎤 SOS Voice"
        VOICE_SOS["flag"] = False

    # Face distress detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray,1.3,5)
    if len(faces)>0:
        face = 1
        status = "😟 Face Distress Detected"

    if gesture or voice or face:
        severity = int(model.predict([[gesture,voice,face]])[0])
        latlon, loc = get_location()
        cur.execute("INSERT INTO emergency VALUES (?,?,?,?,?,?)",
                    (datetime.now().strftime("%H:%M:%S"), "AI", "SOS", severity, loc, "Pending"))
        conn.commit()

    df_sql = pd.read_sql("SELECT * FROM emergency", conn)
    df_sql[['lat','lon']] = df_sql['location'].apply(lambda x: pd.Series(get_location()[0]))
    
    map_html = make_map(df_sql)
    return frame, status, df_sql, map_html

# =========================
# ADMIN
# =========================
def admin_action(action):
    cur.execute("UPDATE emergency SET status=? WHERE rowid=(SELECT max(rowid) FROM emergency)",
                ("Approved" if action=="Approve" else "Cancelled",))
    conn.commit()
    df_sql = pd.read_sql("SELECT * FROM emergency", conn)
    df_sql[['lat','lon']] = df_sql['location'].apply(lambda x: pd.Series(get_location()[0]))
    map_html = make_map(df_sql)
    return df_sql, map_html

# =========================
# WOMEN SAFETY MODE
# =========================
def toggle_women(on):
    WOMEN_MODE["on"] = on
    return "👩 Women Safety Mode ON" if on else "Women Mode OFF"

# =========================
# GRADIO UI
# =========================
with gr.Blocks(theme=gr.themes.Soft(), title="Ultra-Advanced AI Emergency System") as app:
    gr.Markdown("""
# 🚨 Ultra-Advanced Real-Time Emergency System
**Hand ✋ | Voice 🎤 | Face 😟 | GPS 📍 | ML Severity 🤖 | Admin 🛂 | Women Safety 👩**
""")
    with gr.Row():
        cam = gr.Image(source="webcam", streaming=True)
        out = gr.Image()
    status = gr.Textbox(label="Live Status")
    table = gr.Dataframe(label="Emergency Table")
    map_view = gr.HTML(label="Live Map")
    with gr.Row():
        admin = gr.Radio(["Approve","Cancel"], label="Admin Action")
        women = gr.Checkbox(label="👩 Women Safety Mode")

    cam.stream(process, cam, [out,status,table,map_view])
    admin.change(admin_action, admin, [table,map_view])
    women.change(toggle_women, women, status)

app.launch()