Upload 11 files

.gitattributes

@@ -1,35 +1,36 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+alarm.wav filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,171 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# PyPI configuration file
+.pypirc

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Rohit-katkar2003
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,12 @@
----
-title: Drowsiness Detector
-emoji: 🐢
-colorFrom: pink
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.41.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Drowsiness Detector
+emoji: 🐢
+colorFrom: pink
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.41.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

alarm.wav

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5787cf28ed4728fd26c5c7c4c4b658ee0869c3124a36a6a20e404d1046d52f65
+size 1019502

app.py

@@ -0,0 +1,82 @@
+import cv2 as cv
+import numpy as np
+import time
+import pygame
+import streamlit as st
+
+classes = ['Closed', 'Open']  # Class labels for eyes only
+
+# Load face and eye cascade classifiers
+face_cascade = cv.CascadeClassifier("haarcascade files/haarcascade_frontalface_default.xml")
+eye_cascade = cv.CascadeClassifier("haarcascade files/haarcascade_eye.xml")
+
+# Initialize alarm
+pygame.mixer.init()
+pygame.mixer.music.load("alarm.wav")
+
+# Track eye closure duration
+closed_start_time = None
+alarm_triggered = False
+
+# Prepare the frame for eye detection
+def prepare_frame(frame):
+    global closed_start_time, alarm_triggered
+    gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)  # Convert to grayscale for better detection
+    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5)
+    eye_status = 'Closed'
+    
+    for (x, y, w, h) in faces:
+        face_roi = frame[y:y+h, x:x+w]
+        roi_gray = gray_frame[y:y+h, x:x+w]
+        eyes = eye_cascade.detectMultiScale(roi_gray, scaleFactor=1.1, minNeighbors=5)
+        
+        # If two or more eyes detected, classify as 'Open'
+        if len(eyes) >= 2:
+            eye_status = 'Open'
+            if alarm_triggered:
+                pygame.mixer.music.stop()
+                alarm_triggered = False
+            closed_start_time = None
+        else:
+            if closed_start_time is None:
+                closed_start_time = time.time()
+            elif time.time() - closed_start_time >= 3:
+                if not alarm_triggered:
+                    pygame.mixer.music.play()
+                    alarm_triggered = True
+        
+        # Draw a rectangle around the entire face
+        cv.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        break  # Stop after detecting the first face to avoid multiple rectangles
+
+    cv.putText(frame, eye_status, (20, 50), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    return frame, eye_status
+
+# Streamlit interface
+st.title("Real-time Eye Detection and Drowsiness Alert")
+st.markdown("**Close your eyes for 3 seconds to trigger the alarm.**")
+
+FRAME_WINDOW = st.image([])
+run = st.button("Start Detection")
+stop = st.button("Stop Detection")
+
+cap = cv.VideoCapture(0)
+
+while run:
+    ret, frame = cap.read()
+    if not ret:
+        st.warning("Failed to access webcam.")
+        break
+
+    frame = cv.flip(frame, 1)  # Flip frame horizontally
+    frame, eye_status = prepare_frame(frame)
+    frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
+    FRAME_WINDOW.image(frame)
+
+    if stop:
+        cap.release()
+        cv.destroyAllWindows()
+        break
+
+cap.release()
+cv.destroyAllWindows()

research/CV.ipynb

@@ -0,0 +1,539 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os  \n",
+    "import numpy as np \n",
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt  \n",
+    "import matplotlib.image as mimage  \n",
+    "import cv2 as cv  \n",
+    "import random \n",
+    "from PIL import Image\n",
+    " \n",
+    "import warnings \n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'d:\\\\Final _proj\\\\Driver Drawsines\\\\research'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.chdir(\"..\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'d:\\\\Final _proj\\\\Driver Drawsines'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tensorflow.keras.models import load_model \n",
+    "model = load_model(\"drowiness_new6.h5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Final"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For Yawn , no-Yawn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step\n",
+      "[[0.5339012  0.15986206 0.22364037 0.08259626]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step\n",
+      "[[0.55441564 0.16944881 0.19709249 0.07904309]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step\n",
+      "[[0.4521452  0.13079698 0.31140834 0.10564953]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step\n",
+      "[[0.5303667  0.16052838 0.22346936 0.08563555]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step\n",
+      "[[0.5102811  0.15651394 0.24725401 0.08595096]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step\n",
+      "[[0.39522764 0.5094784  0.08734289 0.00795105]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step\n",
+      "[[0.4849061  0.5083012  0.00610906 0.0006836 ]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step\n",
+      "[[4.9457058e-01 4.9904898e-01 6.0036671e-03 3.7674789e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step\n",
+      "[[4.9783722e-01 4.9717689e-01 4.6812119e-03 3.0464309e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step\n",
+      "[[5.2321953e-01 4.6852756e-01 7.8288494e-03 4.2414822e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
+      "[[4.9153149e-01 5.0222409e-01 5.8816043e-03 3.6273352e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step\n",
+      "[[0.54127944 0.44826204 0.00961303 0.00084548]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[0.50393206 0.4863479  0.00865785 0.0010622 ]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step\n",
+      "[[5.4283851e-01 4.5033500e-01 6.4291335e-03 3.9734103e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
+      "[[5.2413237e-01 4.7132316e-01 4.3296791e-03 2.1475746e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step\n",
+      "[[5.1084113e-01 4.8559844e-01 3.2412172e-03 3.1920703e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step\n",
+      "[[5.7073164e-01 4.2556059e-01 3.4928408e-03 2.1485660e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step\n",
+      "[[5.5285156e-01 4.4224325e-01 4.5622713e-03 3.4294103e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
+      "[[0.5427735  0.44871727 0.00793501 0.00057425]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step\n",
+      "[[5.4114133e-01 4.5342380e-01 5.1169088e-03 3.1804194e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step\n",
+      "[[5.7159519e-01 4.2561889e-01 2.5791947e-03 2.0669916e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step\n",
+      "[[5.7468766e-01 4.2153805e-01 3.5532070e-03 2.2111020e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
+      "[[5.3590161e-01 4.5808852e-01 5.6171627e-03 3.9265043e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[5.5657232e-01 4.3508846e-01 7.7924151e-03 5.4685154e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step\n",
+      "[[0.49480158 0.49675417 0.00781286 0.00063132]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step\n",
+      "[[5.5822819e-01 4.3658417e-01 4.8851566e-03 3.0245088e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step\n",
+      "[[0.53632814 0.4521163  0.01081774 0.00073785]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n",
+      "[[0.5257215  0.4666978  0.00665365 0.00092702]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step\n",
+      "[[0.56758076 0.42414907 0.00691421 0.00135598]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[0.5783753  0.41040668 0.01005661 0.00116142]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step\n",
+      "[[0.61831737 0.37240994 0.00795365 0.00131897]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step\n",
+      "[[0.6174236  0.37296996 0.0084237  0.00118283]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[0.53627264 0.4550183  0.00787264 0.00083647]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step\n",
+      "[[6.3499963e-01 3.6145884e-01 3.0721761e-03 4.6933253e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
+      "[[0.59015673 0.3910087  0.01667546 0.00215918]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step\n",
+      "[[0.547751   0.43992066 0.01099813 0.00133022]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step\n",
+      "[[6.1030549e-01 3.8535413e-01 3.9123669e-03 4.2805608e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step\n",
+      "[[0.5895906  0.39260563 0.01642497 0.00137877]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[0.4235793  0.56652987 0.00914386 0.00074703]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step\n",
+      "[[4.8226121e-01 5.0990009e-01 7.3582488e-03 4.8048754e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step\n",
+      "[[0.47468084 0.49073824 0.03259087 0.00199007]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
+      "[[0.48702973 0.5029544  0.00944369 0.00057216]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step\n",
+      "[[0.46537527 0.5274027  0.0066941  0.00052791]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
+      "[[0.425588   0.5606394  0.01319939 0.00057326]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
+      "[[4.6433157e-01 5.3061748e-01 4.6703219e-03 3.8065348e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[5.0271779e-01 4.9165285e-01 5.3839809e-03 2.4538283e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step\n",
+      "[[4.9817729e-01 4.9809283e-01 3.4314308e-03 2.9849299e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step\n",
+      "[[0.4323189  0.55639476 0.01058941 0.00069697]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step\n",
+      "[[4.9058634e-01 5.0517666e-01 3.8562843e-03 3.8071233e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n",
+      "[[4.6864349e-01 5.2765518e-01 3.3698247e-03 3.3153337e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[4.7328150e-01 5.1489401e-01 1.1425589e-02 3.9892178e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step\n",
+      "[[5.6844020e-01 4.2386171e-01 7.2170394e-03 4.8100951e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n",
+      "[[4.7888020e-01 5.1815802e-01 2.7156596e-03 2.4622082e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
+      "[[4.4938755e-01 5.4168677e-01 8.4247021e-03 5.0095661e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step\n",
+      "[[4.8189956e-01 5.1445591e-01 3.2346470e-03 4.0994020e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step\n",
+      "[[4.3830103e-01 5.5796260e-01 3.5162044e-03 2.2019379e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m���━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n",
+      "[[3.8675940e-01 6.1051351e-01 2.5933362e-03 1.3384310e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n",
+      "[[4.7633487e-01 5.1811528e-01 5.2490053e-03 3.0079822e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
+      "[[0.5355513  0.4560149  0.00782073 0.00061306]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step\n",
+      "[[4.9573547e-01 5.0054139e-01 3.4869814e-03 2.3615584e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n",
+      "[[4.6156302e-01 5.3393996e-01 4.1647437e-03 3.3234234e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[0.52708125 0.46093372 0.01130558 0.00067942]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step\n",
+      "[[4.4243312e-01 5.5234385e-01 4.9254801e-03 2.9754813e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[4.3317986e-01 5.6243598e-01 4.1413051e-03 2.4284663e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step\n",
+      "[[0.52935624 0.46102938 0.00886495 0.00074939]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[4.8261899e-01 5.1293612e-01 4.1731801e-03 2.7175286e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step\n",
+      "[[4.4055092e-01 5.5352402e-01 5.6604608e-03 2.6452570e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step\n",
+      "[[4.2110297e-01 5.7347727e-01 5.1973891e-03 2.2236633e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step\n",
+      "[[3.9519185e-01 5.9414124e-01 1.0202706e-02 4.6415642e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[0.52715486 0.42906848 0.04193437 0.00184232]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step\n",
+      "[[0.4206979  0.55904144 0.01885335 0.00140733]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step\n",
+      "[[0.544774   0.14541742 0.25587305 0.05393547]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[0.4370813  0.49844724 0.06200609 0.00246527]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step\n",
+      "[[0.4528224  0.52911925 0.01663947 0.00141889]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step\n",
+      "[[0.3819743  0.5466778  0.06750397 0.00384389]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step\n",
+      "[[0.40970472 0.5569316  0.03105194 0.00231172]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step\n",
+      "[[0.6114612  0.19729745 0.1383356  0.0529057 ]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step\n",
+      "[[0.45113012 0.50088024 0.04475248 0.00323708]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━���━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step\n",
+      "[[0.45435527 0.53013545 0.01427606 0.00123324]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step\n",
+      "[[0.47971782 0.5001755  0.01883741 0.00126928]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step\n",
+      "[[0.54685706 0.43772423 0.01460937 0.00080937]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step\n",
+      "[[0.43435532 0.51326823 0.04905771 0.00331874]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
+      "[[0.4691491  0.51138616 0.01782178 0.00164296]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step\n",
+      "[[0.36563382 0.609435   0.02245951 0.00247164]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step\n",
+      "[[0.4674584  0.51042354 0.0199133  0.00220476]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step\n",
+      "[[0.35535875 0.62192243 0.02078114 0.0019377 ]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step\n",
+      "[[0.35675234 0.5971697  0.04377323 0.00230471]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step\n",
+      "[[0.32445276 0.6365426  0.03643773 0.00256694]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step\n",
+      "[[0.44510007 0.5342694  0.01953486 0.00109567]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
+      "[[0.30969    0.60378057 0.07928306 0.00724639]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step\n",
+      "[[0.56752884 0.35713732 0.07257319 0.00276066]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step\n",
+      "[[0.34694657 0.5160776  0.13258497 0.00439083]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step\n",
+      "[[0.4539524  0.46011406 0.08372305 0.00221045]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step\n",
+      "[[5.1272041e-01 4.3961889e-01 4.7271658e-02 3.8900448e-04]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step\n",
+      "[[0.67923796 0.2438758  0.07580444 0.00108182]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step\n",
+      "[[0.5821022  0.27528477 0.13952196 0.00309112]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step\n",
+      "[[0.4618073  0.35787314 0.16838866 0.01193077]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
+      "[[0.5675023  0.3495956  0.07748043 0.0054217 ]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step\n",
+      "[[0.45714724 0.42329356 0.11500251 0.00455673]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[0.5857304  0.38192827 0.0309583  0.00138313]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step\n",
+      "[[0.5372635  0.43943352 0.02258803 0.00071496]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step\n",
+      "[[0.5059755  0.46730185 0.02545177 0.00127086]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step\n",
+      "[[0.5287636  0.436619   0.03319615 0.00142124]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step\n",
+      "[[0.5353085 0.3997285 0.0617837 0.0031793]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step\n",
+      "[[0.50711626 0.45882738 0.03308126 0.00097504]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step\n",
+      "[[0.47909027 0.4863685  0.03269366 0.00184744]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step\n",
+      "[[0.40050444 0.5718929  0.02541785 0.00218476]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step\n",
+      "[[0.3778685  0.5632252  0.05708069 0.00182558]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step\n",
+      "[[0.57108206 0.40179658 0.02321302 0.00390833]]\n",
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step\n",
+      "[[0.53513265 0.41831055 0.0419606  0.00459616]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cv2 as cv\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "classes = ['no_yawn', 'yawn', 'Closed', 'Open']  # Class labels\n",
+    "IMG_SIZE = 145  # Image size for resizing\n",
+    "face_cascade = cv.CascadeClassifier(\"C:/Users/rohit/Downloads/haarcascade_frontalface_default.xml\")\n",
+    "\n",
+    "# Prepare the frame for prediction\n",
+    "def prepare_frame(frame):\n",
+    "    gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)  # Convert to grayscale for better detection\n",
+    "    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5)\n",
+    "    face_list = []\n",
+    "    coords = []\n",
+    "    \n",
+    "    if len(faces) > 0:\n",
+    "        # Select the face with the maximum area\n",
+    "        f = max(faces, key=lambda x: x[2] * x[3])\n",
+    "        x, y, w, h = f\n",
+    "        face = frame[y:y+h, x:x+w]  # Crop the detected face\n",
+    "        resized_face = cv.resize(face, (IMG_SIZE, IMG_SIZE))  # Resize to match model input\n",
+    "        resized_face = resized_face / 255.0  # Normalize\n",
+    "        face_list.append(resized_face.reshape(-1, IMG_SIZE, IMG_SIZE, 3))\n",
+    "        coords.append((x, y, w, h))\n",
+    "        \n",
+    "    return face_list, coords\n",
+    "\n",
+    "# Start video capture\n",
+    "cap = cv.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    if not ret:\n",
+    "        break\n",
+    "\n",
+    "    frame = cv.flip(frame, 1)  # Flip frame horizontally\n",
+    "    processed_frames, coords = prepare_frame(frame)\n",
+    "    \n",
+    "    if processed_frames:\n",
+    "        for i, processed_frame in enumerate(processed_frames):\n",
+    "            prediction = model.predict(processed_frame) \n",
+    "            print(prediction)\n",
+    "            predicted_class = np.argmax(prediction) \n",
+    "            class_label = classes[predicted_class]\n",
+    "            x, y, w, h = coords[i]\n",
+    "\n",
+    "            # Draw rectangle around the face\n",
+    "            cv.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
+    "            \n",
+    "            # Display predicted label on frame\n",
+    "            cv.putText(frame, f'{class_label}', (x, y-10), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n",
+    "\n",
+    "    # Smoothen video quality by applying a Gaussian blur\n",
+    "    frame = cv.GaussianBlur(frame, (5, 5), 0)\n",
+    "\n",
+    "    cv.imshow('Real-time Emotion Detection', frame)\n",
+    "\n",
+    "    # Press 'q' to quit\n",
+    "    if cv.waitKey(1) & 0xFF == ord('q'):\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv.destroyAllWindows()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap.release()\n",
+    "cv.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### For Eye open or close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pygame 2.6.1 (SDL 2.28.4, Python 3.11.4)\n",
+      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "import cv2 as cv\n",
+    "import numpy as np\n",
+    "import time\n",
+    "import pygame\n",
+    "\n",
+    "classes = ['Closed', 'Open']  # Class labels for eyes only\n",
+    "\n",
+    "# Load face and eye cascade classifiers\n",
+    "face_cascade = cv.CascadeClassifier(\"C:/Users/rohit/Downloads/haarcascade_frontalface_default.xml\")\n",
+    "eye_cascade = cv.CascadeClassifier(\"C:/Users/rohit/Downloads/haarcascade_eye.xml\")\n",
+    "\n",
+    "# Initialize alarm\n",
+    "pygame.mixer.init()\n",
+    "pygame.mixer.music.load(\"research/alarm.wav\")\n",
+    "\n",
+    "# Track eye closure duration\n",
+    "closed_start_time = None\n",
+    "alarm_triggered = False\n",
+    "\n",
+    "# Prepare the frame for eye detection\n",
+    "def prepare_frame(frame):\n",
+    "    global closed_start_time, alarm_triggered\n",
+    "    gray_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)  # Convert to grayscale for better detection\n",
+    "    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5)\n",
+    "    eye_status = 'Closed'\n",
+    "    \n",
+    "    for (x, y, w, h) in faces:\n",
+    "        face_roi = frame[y:y+h, x:x+w]\n",
+    "        roi_gray = gray_frame[y:y+h, x:x+w]\n",
+    "        eyes = eye_cascade.detectMultiScale(roi_gray, scaleFactor=1.1, minNeighbors=5)\n",
+    "        \n",
+    "        # If two or more eyes detected, classify as 'Open'\n",
+    "        if len(eyes) >= 2:\n",
+    "            eye_status = 'Open'\n",
+    "            if alarm_triggered:\n",
+    "                pygame.mixer.music.stop()\n",
+    "                alarm_triggered = False\n",
+    "            closed_start_time = None\n",
+    "        else:\n",
+    "            if closed_start_time is None:\n",
+    "                closed_start_time = time.time()\n",
+    "            elif time.time() - closed_start_time >= 3:\n",
+    "                if not alarm_triggered:\n",
+    "                    pygame.mixer.music.play()\n",
+    "                    alarm_triggered = True\n",
+    "        \n",
+    "        # Draw a rectangle around the entire face\n",
+    "        cv.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
+    "        break  # Stop after detecting the first face to avoid multiple rectangles\n",
+    "\n",
+    "    cv.putText(frame, eye_status, (20, 50), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n",
+    "    return eye_status\n",
+    "\n",
+    "# Start video capture\n",
+    "cap = cv.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret, frame = cap.read()\n",
+    "    if not ret:\n",
+    "        break\n",
+    "\n",
+    "    frame = cv.flip(frame, 1)  # Flip frame horizontally\n",
+    "    eye_status = prepare_frame(frame)\n",
+    "\n",
+    "    # Display the frame with eye detection\n",
+    "    cv.imshow('Real-time Eye Detection', frame)\n",
+    "\n",
+    "    # Press 'q' to quit\n",
+    "    if cv.waitKey(1) & 0xFF == ord('q'):\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv.destroyAllWindows()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chatbot",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}