Create app.py

app.py

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+import streamlit as st
+import cv2
+import mediapipe as mp
+import numpy as np
+import tensorflow as tf
+import tempfile
+
+# Load gesture classification model
+model = tf.keras.models.load_model('gesture_model.h5')
+
+# Mediapipe initialization
+mp_hands = mp.solutions.hands
+hands = mp_hands.Hands()
+mp_draw = mp.solutions.drawing_utils
+
+# Function for gesture classification
+def classify_gesture(landmarks):
+    landmarks = np.array(landmarks).reshape(1, -1)
+    prediction = model.predict(landmarks)
+    return np.argmax(prediction)
+
+# Streamlit UI
+def main():
+    st.set_page_config(page_title="Sign Language Translator", layout="wide")
+    st.title("🤟 Sign Language Translator")
+    st.write("Translate sign language gestures into text and speech in real time.")
+
+    # Sidebar
+    st.sidebar.header("Settings")
+    use_camera = st.sidebar.checkbox("Use Camera")
+
+    # Display Video Feed
+    if use_camera:
+        st.write("### 📸 Live Camera Feed")
+        frame_placeholder = st.empty()
+
+        cap = cv2.VideoCapture(0)
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            results = hands.process(frame)
+
+            if results.multi_hand_landmarks:
+                for hand_landmarks in results.multi_hand_landmarks:
+                    mp_draw.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)
+
+                    # Extract landmarks
+                    landmarks = [landmark.x for landmark in hand_landmarks.landmark]
+                    landmarks += [landmark.y for landmark in hand_landmarks.landmark]
+                    gesture = classify_gesture(landmarks)
+                    st.write(f"Gesture: {gesture}")
+
+            frame_placeholder.image(frame, channels="RGB")
+
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+        cap.release()
+
+if __name__ == "__main__":
+    main()