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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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
+asl_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,130 @@
+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))

asl_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38a2c2b2186178bbbc3cda9d5fb3666dbf2de900f2efeab0dcf93959ee0ebef0
+size 8292972

class_names.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcd5dd14b81322e3811825e0a77c0b649ad8d0be4ba76cce632ba8f13fb8f37c
+size 940