Updated app.py to create a shareable link

app.py

@@ -1,418 +1,418 @@
-import os
-import torch
-import gradio as gr
-import numpy as np
-import soundfile as sf
-from transformers import (
-    AutoModelForSeq2SeqLM,
-    AutoTokenizer,
-    VitsModel,
-    AutoProcessor,
-    AutoModelForCTC,
-    WhisperProcessor,
-    WhisperForConditionalGeneration
-)
-from typing import Optional, Tuple, Dict, List
-
-class TalklasTranslator:
-    """
-    Speech-to-Speech translation pipeline for Philippine languages.
-    Uses MMS/Whisper for STT, NLLB for MT, and MMS for TTS.
-    """
-
-    LANGUAGE_MAPPING = {
-        "English": "eng",
-        "Tagalog": "tgl",
-        "Cebuano": "ceb",
-        "Ilocano": "ilo",
-        "Waray": "war",
-        "Pangasinan": "pag"
-    }
-
-    NLLB_LANGUAGE_CODES = {
-        "eng": "eng_Latn",
-        "tgl": "tgl_Latn",
-        "ceb": "ceb_Latn",
-        "ilo": "ilo_Latn",
-        "war": "war_Latn",
-        "pag": "pag_Latn"
-    }
-
-    def __init__(
-        self,
-        source_lang: str = "eng",
-        target_lang: str = "tgl",
-        device: Optional[str] = None
-    ):
-        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
-        self.source_lang = source_lang
-        self.target_lang = target_lang
-        self.sample_rate = 16000
-
-        print(f"Initializing Talklas Translator on {self.device}")
-
-        # Initialize models
-        self._initialize_stt_model()
-        self._initialize_mt_model()
-        self._initialize_tts_model()
-
-    def _initialize_stt_model(self):
-        """Initialize speech-to-text model with fallback to Whisper"""
-        try:
-            print("Loading STT model...")
-            try:
-                # Try loading MMS model first
-                self.stt_processor = AutoProcessor.from_pretrained("facebook/mms-1b-all")
-                self.stt_model = AutoModelForCTC.from_pretrained("facebook/mms-1b-all")
-
-                # Set language if available
-                if self.source_lang in self.stt_processor.tokenizer.vocab.keys():
-                    self.stt_processor.tokenizer.set_target_lang(self.source_lang)
-                    self.stt_model.load_adapter(self.source_lang)
-                    print(f"Loaded MMS STT model for {self.source_lang}")
-                else:
-                    print(f"Language {self.source_lang} not in MMS, using default")
-
-            except Exception as mms_error:
-                print(f"MMS loading failed: {mms_error}")
-                # Fallback to Whisper
-                print("Loading Whisper as fallback...")
-                self.stt_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
-                self.stt_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
-                print("Loaded Whisper STT model")
-
-            self.stt_model.to(self.device)
-
-        except Exception as e:
-            print(f"STT model initialization failed: {e}")
-            raise RuntimeError("Could not initialize STT model")
-
-    def _initialize_mt_model(self):
-        """Initialize machine translation model"""
-        try:
-            print("Loading NLLB Translation model...")
-            self.mt_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
-            self.mt_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
-            self.mt_model.to(self.device)
-            print("NLLB Translation model loaded")
-        except Exception as e:
-            print(f"MT model initialization failed: {e}")
-            raise
-
-    def _initialize_tts_model(self):
-        """Initialize text-to-speech model"""
-        try:
-            print("Loading TTS model...")
-            try:
-                self.tts_model = VitsModel.from_pretrained(f"facebook/mms-tts-{self.target_lang}")
-                self.tts_tokenizer = AutoTokenizer.from_pretrained(f"facebook/mms-tts-{self.target_lang}")
-                print(f"Loaded TTS model for {self.target_lang}")
-            except Exception as tts_error:
-                print(f"Target language TTS failed: {tts_error}")
-                print("Falling back to English TTS")
-                self.tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng")
-                self.tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
-
-            self.tts_model.to(self.device)
-        except Exception as e:
-            print(f"TTS model initialization failed: {e}")
-            raise
-
-    def update_languages(self, source_lang: str, target_lang: str) -> str:
-        """Update languages and reinitialize models if needed"""
-        if source_lang == self.source_lang and target_lang == self.target_lang:
-            return "Languages already set"
-
-        self.source_lang = source_lang
-        self.target_lang = target_lang
-
-        # Only reinitialize models that depend on language
-        self._initialize_stt_model()
-        self._initialize_tts_model()
-
-        return f"Languages updated to {source_lang} → {target_lang}"
-
-    def speech_to_text(self, audio_path: str) -> str:
-        """Convert speech to text using loaded STT model"""
-        try:
-            waveform, sample_rate = sf.read(audio_path)
-
-            if sample_rate != 16000:
-                import librosa
-                waveform = librosa.resample(waveform, orig_sr=sample_rate, target_sr=16000)
-
-            inputs = self.stt_processor(
-                waveform,
-                sampling_rate=16000,
-                return_tensors="pt"
-            ).to(self.device)
-
-            with torch.no_grad():
-                if isinstance(self.stt_model, WhisperForConditionalGeneration):  # Whisper model
-                    generated_ids = self.stt_model.generate(**inputs)
-                    transcription = self.stt_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
-                else:  # MMS model (Wav2Vec2ForCTC)
-                    logits = self.stt_model(**inputs).logits
-                    predicted_ids = torch.argmax(logits, dim=-1)
-                    transcription = self.stt_processor.batch_decode(predicted_ids)[0]
-
-            return transcription
-
-        except Exception as e:
-            print(f"Speech recognition failed: {e}")
-            raise RuntimeError("Speech recognition failed")
-
-    def translate_text(self, text: str) -> str:
-        """Translate text using NLLB model"""
-        try:
-            source_code = self.NLLB_LANGUAGE_CODES[self.source_lang]
-            target_code = self.NLLB_LANGUAGE_CODES[self.target_lang]
-
-            self.mt_tokenizer.src_lang = source_code
-            inputs = self.mt_tokenizer(text, return_tensors="pt").to(self.device)
-
-            with torch.no_grad():
-                generated_tokens = self.mt_model.generate(
-                    **inputs,
-                    forced_bos_token_id=self.mt_tokenizer.convert_tokens_to_ids(target_code),
-                    max_length=448
-                )
-
-            return self.mt_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-
-        except Exception as e:
-            print(f"Translation failed: {e}")
-            raise RuntimeError("Text translation failed")
-
-    def text_to_speech(self, text: str) -> Tuple[int, np.ndarray]:
-        """Convert text to speech"""
-        try:
-            inputs = self.tts_tokenizer(text, return_tensors="pt").to(self.device)
-
-            with torch.no_grad():
-                output = self.tts_model(**inputs)
-
-            speech = output.waveform.cpu().numpy().squeeze()
-            speech = (speech * 32767).astype(np.int16)
-
-            return self.tts_model.config.sampling_rate, speech
-
-        except Exception as e:
-            print(f"Speech synthesis failed: {e}")
-            raise RuntimeError("Speech synthesis failed")
-
-    def translate_speech(self, audio_path: str) -> Dict:
-        """Full speech-to-speech translation"""
-        try:
-            source_text = self.speech_to_text(audio_path)
-            translated_text = self.translate_text(source_text)
-            sample_rate, audio = self.text_to_speech(translated_text)
-
-            return {
-                "source_text": source_text,
-                "translated_text": translated_text,
-                "output_audio": (sample_rate, audio),
-                "performance": "Translation successful"
-            }
-        except Exception as e:
-            return {
-                "source_text": "Error",
-                "translated_text": "Error",
-                "output_audio": (16000, np.zeros(1000, dtype=np.int16)),
-                "performance": f"Error: {str(e)}"
-            }
-
-    def translate_text_only(self, text: str) -> Dict:
-        """Text-to-speech translation"""
-        try:
-            translated_text = self.translate_text(text)
-            sample_rate, audio = self.text_to_speech(translated_text)
-
-            return {
-                "source_text": text,
-                "translated_text": translated_text,
-                "output_audio": (sample_rate, audio),
-                "performance": "Translation successful"
-            }
-        except Exception as e:
-            return {
-                "source_text": text,
-                "translated_text": "Error",
-                "output_audio": (16000, np.zeros(1000, dtype=np.int16)),
-                "performance": f"Error: {str(e)}"
-            }
-
-class TranslatorSingleton:
-    _instance = None
-
-    @classmethod
-    def get_instance(cls):
-        if cls._instance is None:
-            cls._instance = TalklasTranslator()
-        return cls._instance
-
-def process_audio(audio_path, source_lang, target_lang):
-    """Process audio through the full translation pipeline"""
-    # Validate input
-    if not audio_path:
-        return None, "No audio provided", "No translation available", "Please provide audio input"
-
-    # Update languages
-    source_code = TalklasTranslator.LANGUAGE_MAPPING[source_lang]
-    target_code = TalklasTranslator.LANGUAGE_MAPPING[target_lang]
-
-    translator = TranslatorSingleton.get_instance()
-    status = translator.update_languages(source_code, target_code)
-
-    # Process the audio
-    results = translator.translate_speech(audio_path)
-
-    return results["output_audio"], results["source_text"], results["translated_text"], results["performance"]
-
-def process_text(text, source_lang, target_lang):
-    """Process text through the translation pipeline"""
-    # Validate input
-    if not text:
-        return None, "No text provided", "No translation available", "Please provide text input"
-
-    # Update languages
-    source_code = TalklasTranslator.LANGUAGE_MAPPING[source_lang]
-    target_code = TalklasTranslator.LANGUAGE_MAPPING[target_lang]
-
-    translator = TranslatorSingleton.get_instance()
-    status = translator.update_languages(source_code, target_code)
-
-    # Process the text
-    results = translator.translate_text_only(text)
-
-    return results["output_audio"], results["source_text"], results["translated_text"], results["performance"]
-
-def create_gradio_interface():
-    """Create and launch Gradio interface"""
-    # Define language options
-    languages = list(TalklasTranslator.LANGUAGE_MAPPING.keys())
-
-    # Define the interface
-    demo = gr.Blocks(title="Talklas - Speech & Text Translation")
-
-    with demo:
-        gr.Markdown("# Talklas: Speech-to-Speech Translation System")
-        gr.Markdown("### Translate between Philippine Languages and English")
-
-        with gr.Row():
-            with gr.Column():
-                source_lang = gr.Dropdown(
-                    choices=languages,
-                    value="English",
-                    label="Source Language"
-                )
-
-                target_lang = gr.Dropdown(
-                    choices=languages,
-                    value="Tagalog",
-                    label="Target Language"
-                )
-
-                language_status = gr.Textbox(label="Language Status")
-                update_btn = gr.Button("Update Languages")
-
-        with gr.Tabs():
-            with gr.TabItem("Audio Input"):
-                with gr.Row():
-                    with gr.Column():
-                        gr.Markdown("### Audio Input")
-                        audio_input = gr.Audio(
-                            type="filepath",
-                            label="Upload Audio File"
-                        )
-                        audio_translate_btn = gr.Button("Translate Audio", variant="primary")
-
-                    with gr.Column():
-                        gr.Markdown("### Output")
-                        audio_output = gr.Audio(
-                            label="Translated Speech",
-                            type="numpy",
-                            autoplay=True
-                        )
-
-            with gr.TabItem("Text Input"):
-                with gr.Row():
-                    with gr.Column():
-                        gr.Markdown("### Text Input")
-                        text_input = gr.Textbox(
-                            label="Enter text to translate",
-                            lines=3
-                        )
-                        text_translate_btn = gr.Button("Translate Text", variant="primary")
-
-                    with gr.Column():
-                        gr.Markdown("### Output")
-                        text_output = gr.Audio(
-                            label="Translated Speech",
-                            type="numpy",
-                            autoplay=True
-                        )
-
-        with gr.Row():
-            with gr.Column():
-                source_text = gr.Textbox(label="Source Text")
-                translated_text = gr.Textbox(label="Translated Text")
-                performance_info = gr.Textbox(label="Performance Metrics")
-
-        # Set up events
-        update_btn.click(
-            lambda source_lang, target_lang: TranslatorSingleton.get_instance().update_languages(
-                TalklasTranslator.LANGUAGE_MAPPING[source_lang],
-                TalklasTranslator.LANGUAGE_MAPPING[target_lang]
-            ),
-            inputs=[source_lang, target_lang],
-            outputs=[language_status]
-        )
-
-        # Audio translate button click
-        audio_translate_btn.click(
-            process_audio,
-            inputs=[audio_input, source_lang, target_lang],
-            outputs=[audio_output, source_text, translated_text, performance_info]
-        ).then(
-            None,
-            None,
-            None,
-            js="""() => {
-                const audioElements = document.querySelectorAll('audio');
-                if (audioElements.length > 0) {
-                    const lastAudio = audioElements[audioElements.length - 1];
-                    lastAudio.play().catch(error => {
-                        console.warn('Autoplay failed:', error);
-                        alert('Audio may require user interaction to play');
-                    });
-                }
-            }"""
-        )
-
-        # Text translate button click
-        text_translate_btn.click(
-            process_text,
-            inputs=[text_input, source_lang, target_lang],
-            outputs=[text_output, source_text, translated_text, performance_info]
-        ).then(
-            None,
-            None,
-            None,
-            js="""() => {
-                const audioElements = document.querySelectorAll('audio');
-                if (audioElements.length > 0) {
-                    const lastAudio = audioElements[audioElements.length - 1];
-                    lastAudio.play().catch(error => {
-                        console.warn('Autoplay failed:', error);
-                        alert('Audio may require user interaction to play');
-                    });
-                }
-            }"""
-        )
-
-    return demo
-
-if __name__ == "__main__":
-    demo = create_gradio_interface()
-    demo.launch(share=False)
+import os
+import torch
+import gradio as gr
+import numpy as np
+import soundfile as sf
+from transformers import (
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    VitsModel,
+    AutoProcessor,
+    AutoModelForCTC,
+    WhisperProcessor,
+    WhisperForConditionalGeneration
+)
+from typing import Optional, Tuple, Dict, List
+
+class TalklasTranslator:
+    """
+    Speech-to-Speech translation pipeline for Philippine languages.
+    Uses MMS/Whisper for STT, NLLB for MT, and MMS for TTS.
+    """
+
+    LANGUAGE_MAPPING = {
+        "English": "eng",
+        "Tagalog": "tgl",
+        "Cebuano": "ceb",
+        "Ilocano": "ilo",
+        "Waray": "war",
+        "Pangasinan": "pag"
+    }
+
+    NLLB_LANGUAGE_CODES = {
+        "eng": "eng_Latn",
+        "tgl": "tgl_Latn",
+        "ceb": "ceb_Latn",
+        "ilo": "ilo_Latn",
+        "war": "war_Latn",
+        "pag": "pag_Latn"
+    }
+
+    def __init__(
+        self,
+        source_lang: str = "eng",
+        target_lang: str = "tgl",
+        device: Optional[str] = None
+    ):
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+        self.source_lang = source_lang
+        self.target_lang = target_lang
+        self.sample_rate = 16000
+
+        print(f"Initializing Talklas Translator on {self.device}")
+
+        # Initialize models
+        self._initialize_stt_model()
+        self._initialize_mt_model()
+        self._initialize_tts_model()
+
+    def _initialize_stt_model(self):
+        """Initialize speech-to-text model with fallback to Whisper"""
+        try:
+            print("Loading STT model...")
+            try:
+                # Try loading MMS model first
+                self.stt_processor = AutoProcessor.from_pretrained("facebook/mms-1b-all")
+                self.stt_model = AutoModelForCTC.from_pretrained("facebook/mms-1b-all")
+
+                # Set language if available
+                if self.source_lang in self.stt_processor.tokenizer.vocab.keys():
+                    self.stt_processor.tokenizer.set_target_lang(self.source_lang)
+                    self.stt_model.load_adapter(self.source_lang)
+                    print(f"Loaded MMS STT model for {self.source_lang}")
+                else:
+                    print(f"Language {self.source_lang} not in MMS, using default")
+
+            except Exception as mms_error:
+                print(f"MMS loading failed: {mms_error}")
+                # Fallback to Whisper
+                print("Loading Whisper as fallback...")
+                self.stt_processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+                self.stt_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+                print("Loaded Whisper STT model")
+
+            self.stt_model.to(self.device)
+
+        except Exception as e:
+            print(f"STT model initialization failed: {e}")
+            raise RuntimeError("Could not initialize STT model")
+
+    def _initialize_mt_model(self):
+        """Initialize machine translation model"""
+        try:
+            print("Loading NLLB Translation model...")
+            self.mt_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+            self.mt_tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+            self.mt_model.to(self.device)
+            print("NLLB Translation model loaded")
+        except Exception as e:
+            print(f"MT model initialization failed: {e}")
+            raise
+
+    def _initialize_tts_model(self):
+        """Initialize text-to-speech model"""
+        try:
+            print("Loading TTS model...")
+            try:
+                self.tts_model = VitsModel.from_pretrained(f"facebook/mms-tts-{self.target_lang}")
+                self.tts_tokenizer = AutoTokenizer.from_pretrained(f"facebook/mms-tts-{self.target_lang}")
+                print(f"Loaded TTS model for {self.target_lang}")
+            except Exception as tts_error:
+                print(f"Target language TTS failed: {tts_error}")
+                print("Falling back to English TTS")
+                self.tts_model = VitsModel.from_pretrained("facebook/mms-tts-eng")
+                self.tts_tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")
+
+            self.tts_model.to(self.device)
+        except Exception as e:
+            print(f"TTS model initialization failed: {e}")
+            raise
+
+    def update_languages(self, source_lang: str, target_lang: str) -> str:
+        """Update languages and reinitialize models if needed"""
+        if source_lang == self.source_lang and target_lang == self.target_lang:
+            return "Languages already set"
+
+        self.source_lang = source_lang
+        self.target_lang = target_lang
+
+        # Only reinitialize models that depend on language
+        self._initialize_stt_model()
+        self._initialize_tts_model()
+
+        return f"Languages updated to {source_lang} → {target_lang}"
+
+    def speech_to_text(self, audio_path: str) -> str:
+        """Convert speech to text using loaded STT model"""
+        try:
+            waveform, sample_rate = sf.read(audio_path)
+
+            if sample_rate != 16000:
+                import librosa
+                waveform = librosa.resample(waveform, orig_sr=sample_rate, target_sr=16000)
+
+            inputs = self.stt_processor(
+                waveform,
+                sampling_rate=16000,
+                return_tensors="pt"
+            ).to(self.device)
+
+            with torch.no_grad():
+                if isinstance(self.stt_model, WhisperForConditionalGeneration):  # Whisper model
+                    generated_ids = self.stt_model.generate(**inputs)
+                    transcription = self.stt_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+                else:  # MMS model (Wav2Vec2ForCTC)
+                    logits = self.stt_model(**inputs).logits
+                    predicted_ids = torch.argmax(logits, dim=-1)
+                    transcription = self.stt_processor.batch_decode(predicted_ids)[0]
+
+            return transcription
+
+        except Exception as e:
+            print(f"Speech recognition failed: {e}")
+            raise RuntimeError("Speech recognition failed")
+
+    def translate_text(self, text: str) -> str:
+        """Translate text using NLLB model"""
+        try:
+            source_code = self.NLLB_LANGUAGE_CODES[self.source_lang]
+            target_code = self.NLLB_LANGUAGE_CODES[self.target_lang]
+
+            self.mt_tokenizer.src_lang = source_code
+            inputs = self.mt_tokenizer(text, return_tensors="pt").to(self.device)
+
+            with torch.no_grad():
+                generated_tokens = self.mt_model.generate(
+                    **inputs,
+                    forced_bos_token_id=self.mt_tokenizer.convert_tokens_to_ids(target_code),
+                    max_length=448
+                )
+
+            return self.mt_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+
+        except Exception as e:
+            print(f"Translation failed: {e}")
+            raise RuntimeError("Text translation failed")
+
+    def text_to_speech(self, text: str) -> Tuple[int, np.ndarray]:
+        """Convert text to speech"""
+        try:
+            inputs = self.tts_tokenizer(text, return_tensors="pt").to(self.device)
+
+            with torch.no_grad():
+                output = self.tts_model(**inputs)
+
+            speech = output.waveform.cpu().numpy().squeeze()
+            speech = (speech * 32767).astype(np.int16)
+
+            return self.tts_model.config.sampling_rate, speech
+
+        except Exception as e:
+            print(f"Speech synthesis failed: {e}")
+            raise RuntimeError("Speech synthesis failed")
+
+    def translate_speech(self, audio_path: str) -> Dict:
+        """Full speech-to-speech translation"""
+        try:
+            source_text = self.speech_to_text(audio_path)
+            translated_text = self.translate_text(source_text)
+            sample_rate, audio = self.text_to_speech(translated_text)
+
+            return {
+                "source_text": source_text,
+                "translated_text": translated_text,
+                "output_audio": (sample_rate, audio),
+                "performance": "Translation successful"
+            }
+        except Exception as e:
+            return {
+                "source_text": "Error",
+                "translated_text": "Error",
+                "output_audio": (16000, np.zeros(1000, dtype=np.int16)),
+                "performance": f"Error: {str(e)}"
+            }
+
+    def translate_text_only(self, text: str) -> Dict:
+        """Text-to-speech translation"""
+        try:
+            translated_text = self.translate_text(text)
+            sample_rate, audio = self.text_to_speech(translated_text)
+
+            return {
+                "source_text": text,
+                "translated_text": translated_text,
+                "output_audio": (sample_rate, audio),
+                "performance": "Translation successful"
+            }
+        except Exception as e:
+            return {
+                "source_text": text,
+                "translated_text": "Error",
+                "output_audio": (16000, np.zeros(1000, dtype=np.int16)),
+                "performance": f"Error: {str(e)}"
+            }
+
+class TranslatorSingleton:
+    _instance = None
+
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = TalklasTranslator()
+        return cls._instance
+
+def process_audio(audio_path, source_lang, target_lang):
+    """Process audio through the full translation pipeline"""
+    # Validate input
+    if not audio_path:
+        return None, "No audio provided", "No translation available", "Please provide audio input"
+
+    # Update languages
+    source_code = TalklasTranslator.LANGUAGE_MAPPING[source_lang]
+    target_code = TalklasTranslator.LANGUAGE_MAPPING[target_lang]
+
+    translator = TranslatorSingleton.get_instance()
+    status = translator.update_languages(source_code, target_code)
+
+    # Process the audio
+    results = translator.translate_speech(audio_path)
+
+    return results["output_audio"], results["source_text"], results["translated_text"], results["performance"]
+
+def process_text(text, source_lang, target_lang):
+    """Process text through the translation pipeline"""
+    # Validate input
+    if not text:
+        return None, "No text provided", "No translation available", "Please provide text input"
+
+    # Update languages
+    source_code = TalklasTranslator.LANGUAGE_MAPPING[source_lang]
+    target_code = TalklasTranslator.LANGUAGE_MAPPING[target_lang]
+
+    translator = TranslatorSingleton.get_instance()
+    status = translator.update_languages(source_code, target_code)
+
+    # Process the text
+    results = translator.translate_text_only(text)
+
+    return results["output_audio"], results["source_text"], results["translated_text"], results["performance"]
+
+def create_gradio_interface():
+    """Create and launch Gradio interface"""
+    # Define language options
+    languages = list(TalklasTranslator.LANGUAGE_MAPPING.keys())
+
+    # Define the interface
+    demo = gr.Blocks(title="Talklas - Speech & Text Translation")
+
+    with demo:
+        gr.Markdown("# Talklas: Speech-to-Speech Translation System")
+        gr.Markdown("### Translate between Philippine Languages and English")
+
+        with gr.Row():
+            with gr.Column():
+                source_lang = gr.Dropdown(
+                    choices=languages,
+                    value="English",
+                    label="Source Language"
+                )
+
+                target_lang = gr.Dropdown(
+                    choices=languages,
+                    value="Tagalog",
+                    label="Target Language"
+                )
+
+                language_status = gr.Textbox(label="Language Status")
+                update_btn = gr.Button("Update Languages")
+
+        with gr.Tabs():
+            with gr.TabItem("Audio Input"):
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("### Audio Input")
+                        audio_input = gr.Audio(
+                            type="filepath",
+                            label="Upload Audio File"
+                        )
+                        audio_translate_btn = gr.Button("Translate Audio", variant="primary")
+
+                    with gr.Column():
+                        gr.Markdown("### Output")
+                        audio_output = gr.Audio(
+                            label="Translated Speech",
+                            type="numpy",
+                            autoplay=True
+                        )
+
+            with gr.TabItem("Text Input"):
+                with gr.Row():
+                    with gr.Column():
+                        gr.Markdown("### Text Input")
+                        text_input = gr.Textbox(
+                            label="Enter text to translate",
+                            lines=3
+                        )
+                        text_translate_btn = gr.Button("Translate Text", variant="primary")
+
+                    with gr.Column():
+                        gr.Markdown("### Output")
+                        text_output = gr.Audio(
+                            label="Translated Speech",
+                            type="numpy",
+                            autoplay=True
+                        )
+
+        with gr.Row():
+            with gr.Column():
+                source_text = gr.Textbox(label="Source Text")
+                translated_text = gr.Textbox(label="Translated Text")
+                performance_info = gr.Textbox(label="Performance Metrics")
+
+        # Set up events
+        update_btn.click(
+            lambda source_lang, target_lang: TranslatorSingleton.get_instance().update_languages(
+                TalklasTranslator.LANGUAGE_MAPPING[source_lang],
+                TalklasTranslator.LANGUAGE_MAPPING[target_lang]
+            ),
+            inputs=[source_lang, target_lang],
+            outputs=[language_status]
+        )
+
+        # Audio translate button click
+        audio_translate_btn.click(
+            process_audio,
+            inputs=[audio_input, source_lang, target_lang],
+            outputs=[audio_output, source_text, translated_text, performance_info]
+        ).then(
+            None,
+            None,
+            None,
+            js="""() => {
+                const audioElements = document.querySelectorAll('audio');
+                if (audioElements.length > 0) {
+                    const lastAudio = audioElements[audioElements.length - 1];
+                    lastAudio.play().catch(error => {
+                        console.warn('Autoplay failed:', error);
+                        alert('Audio may require user interaction to play');
+                    });
+                }
+            }"""
+        )
+
+        # Text translate button click
+        text_translate_btn.click(
+            process_text,
+            inputs=[text_input, source_lang, target_lang],
+            outputs=[text_output, source_text, translated_text, performance_info]
+        ).then(
+            None,
+            None,
+            None,
+            js="""() => {
+                const audioElements = document.querySelectorAll('audio');
+                if (audioElements.length > 0) {
+                    const lastAudio = audioElements[audioElements.length - 1];
+                    lastAudio.play().catch(error => {
+                        console.warn('Autoplay failed:', error);
+                        alert('Audio may require user interaction to play');
+                    });
+                }
+            }"""
+        )
+
+    return demo
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch(share=True)