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0b52452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image
import pyttsx3
# ------------------- CONFIG -------------------
MODEL_PATH = "asl_model.keras" # or "asl_model.h5"
CLASS_NAMES_PATH = "class_names.npy"
IMG_HEIGHT, IMG_WIDTH = 64, 64
# ------------------- LOAD MODEL -------------------
@st.cache_resource
def load_model():
return tf.keras.models.load_model(MODEL_PATH)
@st.cache_data
def load_class_names():
return np.load(CLASS_NAMES_PATH)
model = load_model()
class_names = load_class_names()
# ------------------- SPEAK FUNCTION -------------------
def speak(text):
engine = pyttsx3.init()
voices = engine.getProperty('voices')
engine.setProperty('voice', voices[0].id) # Change to voices[1].id for female
engine.setProperty('rate', 150)
engine.setProperty('volume', 1.0)
engine.say(text)
engine.runAndWait()
# ------------------- PREDICTION FUNCTION -------------------
def predict(image):
image = image.resize((IMG_WIDTH, IMG_HEIGHT))
img_array = tf.keras.utils.img_to_array(image)
img_array = tf.expand_dims(img_array, 0) / 255.0
prediction = model.predict(img_array)
index = np.argmax(prediction[0])
label = class_names[index]
confidence = np.max(prediction[0])
return label, confidence
# ------------------- STREAMLIT UI -------------------
st.set_page_config(page_title="ASL Sign Classifier", page_icon="🤟", layout="centered")
# ---------- CSS Styling ----------
st.markdown("""
<style>
.main-title {
font-size: 2.8em;
text-align: center;
font-weight: bold;
margin-bottom: 10px;
color: #333;
}
.description {
text-align: center;
font-size: 1.1rem;
margin-bottom: 20px;
color: #555;
}
.prediction-box {
background-color: #f0f2f6;
padding: 1.5rem;
border-radius: 12px;
text-align: center;
box-shadow: 0 4px 12px rgba(0,0,0,0.1);
font-size: 1.2rem;
color: #000;
}
.stButton > button {
border-radius: 8px;
padding: 0.6rem 1.2rem;
font-weight: 500;
}
</style>
""", unsafe_allow_html=True)
# ---------- Title ----------
st.markdown('<div class="main-title">🤟 ASL Sign Language Classifier</div>', unsafe_allow_html=True)
st.markdown('<div class="description">Upload an image of an ASL hand sign and get an instant spoken prediction!</div>', unsafe_allow_html=True)
# ---------- File Upload ----------
uploaded_file = st.file_uploader("📁 Upload ASL Image", type=["jpg", "png", "jpeg"])
# ---------- Prediction Flow ----------
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="🖼 Uploaded Image", use_column_width=False, width=250)
if st.button("🎯 Predict Sign"):
with st.spinner("Analyzing the sign..."):
label, confidence = predict(image)
# Handle special signs
spoken_label = str(label)
if spoken_label == "SPACE":
spoken_label = "space"
elif spoken_label == "DELETE":
spoken_label = "delete"
elif spoken_label == "NOTHING":
spoken_label = "nothing"
# Save prediction
st.session_state['prediction'] = {
'label': label,
'confidence': confidence
}
# Automatically speak
speak(spoken_label)
# ---------- Show Result ----------
if 'prediction' in st.session_state:
label = st.session_state['prediction']['label']
confidence = st.session_state['prediction']['confidence']
st.markdown(f"""
<div class="prediction-box">
✅ <strong>Predicted Sign:</strong>
<span style="font-size: 1.5rem; font-weight: bold;">{label}</span><br><br>
🔍 <strong>Confidence:</strong> {confidence*100:.2f}%
</div>
""", unsafe_allow_html=True)
# Optional: Repeat audio
if st.button("🔊 Repeat Prediction"):
speak(str(label))
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