# Sign Language Translator with Streamlit

import streamlit as st
import cv2
import numpy as np
import mediapipe as mp

# Load Mediapipe Hands model
mp_hands = mp.solutions.hands
hands = mp_hands.Hands()

# Function to process webcam feed
def process_webcam():
    cap = cv2.VideoCapture(0)
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        # Convert the frame to RGB
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        results = hands.process(frame_rgb)

        # Draw hand landmarks
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                mp.solutions.drawing_utils.draw_landmarks(frame, hand_landmarks, mp_hands.HAND_CONNECTIONS)

        # Display the frame
        cv2.imshow('Webcam', frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

# Streamlit UI
st.title("Sign Language Translator")
st.write("This application translates sign language into Farsi and provides visual aids for understanding.")

if st.button("Open Webcam"):
    st.write("Opening webcam...")
    process_webcam()

# Placeholder for translation logic
def translate_sign_language(hand_gesture):
    # Placeholder for actual translation logic
    return "Translated Text in Farsi"

# Placeholder for visual aid
def show_visual_aid(hand_gesture):
    # Placeholder for actual visual aid logic
    st.image("path_to_hand_shape_image.png", caption="Hand Shape for Sign Language")

# User input for non-sign language users
user_input = st.text_input("Enter text if you do not know sign language:")
if user_input:
    st.write("You entered:", user_input)
    # Logic to show visual aid
    show_visual_aid(user_input)