import os
import sys
import gradio as gr

# Install required packages if not already installed
try:
    import sign_language_translator as slt
    import requests
except ImportError:
    print("Installing required packages...")
    os.system("pip install sign-language-translator requests --quiet")
    import sign_language_translator as slt
    import requests

TITLE = "English to Pakistan Sign Language Translator"
DESCRIPTION = """This app translates English text to Pakistan Sign Language using two approaches:
1. Direct English to Pakistan Sign Language translation
2. English → Urdu → Pakistan Sign Language (as a fallback)

**NOTE:**
- The app will first try direct translation, then fallback to Urdu if needed
- Some simple phrases work better with direct translation
- For best results, use simple sentences
"""

# Common English phrases with their Urdu translations
COMMON_PHRASES = {
    "how are you": "آپ کیسے ہیں",
    "hello": "السلام علیکم",
    "thank you": "شکریہ",
    "good morning": "صبح بخیر",
    "good afternoon": "دوپہر بخیر",
    "good evening": "شام بخیر",
    "good night": "شب بخیر",
    "my name is": "میرا نام ہے",
    "what is your name": "آپ کا نام کیا ہے",
    "nice to meet you": "آپ سے مل کر خوشی ہوئی",
    "i am fine": "میں ٹھیک ہوں",
    "yes": "ہاں",
    "no": "نہیں",
    "please": "براہ کرم",
    "sorry": "معذرت",
    "goodbye": "خدا حافظ",
    "i love you": "میں تم سے پیار کرتا ہوں",
    "welcome": "خوش آمدید",
    "excuse me": "معذرت",
    "help": "مدد",
    "where is": "کہاں ہے",
    "what time is it": "کیا وقت ہوا ہے",
    "how much is this": "یہ کتنے کا ہے",
    "i understand": "میں سمجھتا ہوں",
    "i don't understand": "میں نہیں سمجھتا",
}

# Global model variables
en_model = None
ur_model = None

def get_models():
    """Initialize both translation models"""
    try:
        print("Loading English model...")
        en_model = slt.models.ConcatenativeSynthesis("en", "pk-sl", "video")
        print("Loading Urdu model...")
        ur_model = slt.models.ConcatenativeSynthesis("ur", "pk-sl", "video")
        return en_model, ur_model
    except Exception as e:
        print(f"Error initializing models: {str(e)}")
        return None, None

def initialize_models():
    """Ensure models are loaded"""
    global en_model, ur_model
    if en_model is None or ur_model is None:
        en_model, ur_model = get_models()
    return en_model is not None and ur_model is not None

def check_phrase_match(text):
    """Check if input matches any common phrases"""
    text = text.lower().strip()
    
    # Direct matches
    if text in COMMON_PHRASES:
        return COMMON_PHRASES[text]
    
    # Check for partial matches at the beginning of the phrase
    for eng, urdu in COMMON_PHRASES.items():
        if text.startswith(eng + " "):
            return urdu + text[len(eng):]
    
    return None

def english_to_urdu(text):
    """Translate English text to Urdu"""
    if not text:
        return "", "Please enter text to translate"
    
    # First check our custom dictionary
    phrase_match = check_phrase_match(text.lower())
    if phrase_match:
        return phrase_match, f"Using known translation: {phrase_match}"
    
    # Try multiple translation APIs for better reliability
    try:
        # First try Google Translate API via a proxy endpoint
        url = "https://translate.googleapis.com/translate_a/single"
        params = {
            "client": "gtx",
            "sl": "en",
            "tl": "ur",
            "dt": "t",
            "q": text
        }
        
        response = requests.get(url, params=params)
        if response.status_code == 200:
            data = response.json()
            try:
                # Extract translation from response
                translated_text = ''.join([sent[0] for sent in data[0]])
                return translated_text, f"Translated to Urdu: {translated_text}"
            except:
                pass
            
        # Fallback to MyMemory API
        url = f"https://api.mymemory.translated.net/get?q={text}&langpair=en|ur"
        response = requests.get(url)
        data = response.json()
        
        if "responseData" in data and "translatedText" in data["responseData"]:
            urdu_text = data["responseData"]["translatedText"]
            return urdu_text, f"Translated to Urdu: {urdu_text}"
            
        return "", "Error: Could not translate to Urdu"
    
    except Exception as e:
        print(f"Translation API error: {str(e)}")
        return "", f"Error during Urdu translation: {str(e)}"

def english_to_sign_direct(english_text, format_type):
    """Try direct English to Sign Language translation"""
    if not english_text:
        return None, "No English text to translate"
    
    # Initialize models if needed
    if not initialize_models():
        return None, "Failed to initialize models"
    
    try:
        # Convert first letter to lowercase (required by model)
        text = english_text[:1].lower() + english_text[1:] if english_text else ""
        
        # Configure English model
        en_model.text_language = "en"
        en_model.sign_language = "pk-sl"
        en_model.sign_format = format_type
        
        if format_type == "landmarks":
            en_model.sign_embedding_model = "mediapipe-world"
        
        # Translate directly
        output_path = "output_en.mp4"
        sign = en_model.translate(text)
        
        # Save output
        if isinstance(sign, slt.Landmarks):
            sign.data[:, 33:54, :3] += -sign.data[:, 33:34, :3] + sign.data[:, 15:16, :3]
            sign.data[:, 54:, :3] += -sign.data[:, 54:55, :3] + sign.data[:, 16:17, :3]
            sign.save_animation(output_path, overwrite=True)
        else:
            sign.save(output_path, overwrite=True, codec="mp4v")
        
        return output_path, "Direct translation successful"
    
    except Exception as e:
        error_msg = str(e)
        print(f"Direct translation error: {error_msg}")
        return None, f"Direct translation failed: {error_msg}"

def urdu_to_sign(urdu_text, format_type):
    """Translate Urdu text to Pakistan Sign Language"""
    if not urdu_text:
        return None, "No Urdu text to translate"
    
    # Initialize models if needed
    if not initialize_models():
        return None, "Failed to initialize models"
    
    try:
        # Configure Urdu model
        ur_model.text_language = "ur"
        ur_model.sign_language = "pk-sl"
        ur_model.sign_format = format_type
        
        if format_type == "landmarks":
            ur_model.sign_embedding_model = "mediapipe-world"
        
        # Translate
        output_path = "output_ur.mp4"
        sign = ur_model.translate(urdu_text)
        
        # Save output
        if isinstance(sign, slt.Landmarks):
            sign.data[:, 33:54, :3] += -sign.data[:, 33:34, :3] + sign.data[:, 15:16, :3]
            sign.data[:, 54:, :3] += -sign.data[:, 54:55, :3] + sign.data[:, 16:17, :3]
            sign.save_animation(output_path, overwrite=True)
        else:
            sign.save(output_path, overwrite=True, codec="mp4v")
        
        return output_path, "Urdu translation successful"
    
    except Exception as e:
        error_msg = str(e)
        print(f"Urdu translation error: {error_msg}")
        return None, f"Urdu translation failed: {error_msg}"

def translate_english_to_sign(english_text, format_type):
    """Complete translation pipeline with fallback option"""
    if not english_text:
        return None, "", ""
    
    # Try direct translation first
    video, direct_status = english_to_sign_direct(english_text, format_type)
    
    # If direct translation works, return it
    if video:
        return video, "", f"English: \"{english_text}\"\nDirect translation: {direct_status}"
    
    # If direct translation fails, try via Urdu
    urdu_text, urdu_status = english_to_urdu(english_text)
    if not urdu_text:
        return None, "", f"English: \"{english_text}\"\nDirect translation failed\n{urdu_status}"
    
    # Translate Urdu to Sign Language
    video, sign_status = urdu_to_sign(urdu_text, format_type)
    
    # Combine status messages
    status = f"English: \"{english_text}\"\nDirect translation failed\nUsing Urdu: \"{urdu_text}\"\n{sign_status}"
    
    return video, urdu_text, status

# Create the Gradio interface
with gr.Blocks(title=TITLE) as demo:
    gr.Markdown(f"# {TITLE}")
    gr.Markdown(DESCRIPTION)
    
    with gr.Row():
        with gr.Column():
            # Input area
            english_input = gr.Textbox(
                lines=4,
                placeholder="Enter English text here...",
                label="English Text"
            )
            
            format_dropdown = gr.Dropdown(
                choices=["video", "landmarks"],
                value="video",
                label="Output Format"
            )
            
            with gr.Row():
                clear_btn = gr.Button("Clear")
                translate_btn = gr.Button("Translate", variant="primary")
            
            # Intermediate Urdu translation (may be empty if direct translation works)
            urdu_output = gr.Textbox(label="Urdu Translation (if used)", interactive=False)
            
            # Status area
            status_output = gr.Textbox(label="Status", interactive=False)
        
        with gr.Column():
            # Output video
            video_output = gr.Video(
                label="Sign Language Output",
                format="mp4",
                autoplay=True,
                show_download_button=True
            )
    
    # Examples that work well with this model
    gr.Examples(
        examples=[
            ["we are here", "video"],
            ["thank you", "video"],
            ["good morning", "video"],
            ["yes", "video"]
        ],
        inputs=[english_input, format_dropdown],
        outputs=[video_output, urdu_output, status_output],
        fn=translate_english_to_sign
    )
    
    # Event handlers
    translate_btn.click(
        fn=translate_english_to_sign,
        inputs=[english_input, format_dropdown],
        outputs=[video_output, urdu_output, status_output]
    )
    
    clear_btn.click(
        fn=lambda: ("", "", "Input cleared"),
        inputs=None,
        outputs=[english_input, urdu_output, status_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch()