Create app.py

app.py

@@ -0,0 +1,49 @@
+import os
+from transformers import pipeline
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+# Initialize the translation pipeline
+translation_pipeline = pipeline("translation", model="facebook/nllb-200-distilled-600M")
+
+# Initialize the text-to-speech client
+# Make sure you have HF_TOKEN set as a secret in your Hugging Face Space
+client = InferenceClient(
+    provider="fal-ai",
+    api_key=os.environ.get("HF_TOKEN"),
+)
+
+def translate_and_speak(chinese_text):
+    """
+    Translates Chinese text to English and generates speech.
+    """
+    # Translate the text
+    translated_text = translation_pipeline(chinese_text)[0]['translation_text']
+
+    # Generate speech from the translated text
+    # Use a try-except block to handle potential errors during speech generation
+    try:
+        audio_bytes = client.text_to_speech(
+            translated_text,
+            model="hexgrad/Kokoro-82M",
+        )
+        return translated_text, audio_bytes
+    except Exception as e:
+        return translated_text, f"Error generating speech: {e}"
+
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=translate_and_speak,
+    inputs=gr.Textbox(label="Enter Chinese Text"),
+    outputs=[
+        gr.Textbox(label="Translated English Text"),
+        gr.Audio(label="Generated Speech", format="wav")
+    ],
+    title="Chinese to English Translation and Text-to-Speech",
+    description="Translate Chinese text to English and listen to the English translation."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()