Update app.py

app.py

@@ -1,64 +1,105 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import asyncio
+import wave
+import os
+from google import genai
+from google.genai import types
+
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+client = genai.Client(http_options={'api_version': 'v1alpha'}, api_key=GOOGLE_API_KEY)
+
+
+# Save PCM audio to WAV file
+def save_wave_file(filename, pcm, channels=1, rate=24000, sample_width=2):
+    with wave.open(filename, "wb") as wf:
+        wf.setnchannels(channels)
+        wf.setsampwidth(sample_width)
+        wf.setframerate(rate)
+        wf.writeframes(pcm)
+
+# Async music generation
+async def generate_music(prompt, bpm, temperature):
+    audio_chunks = []
+
+    async def receive_audio(session):
+        async for message in session.receive():
+            chunk = message.server_content.audio_chunks[0].data
+            audio_chunks.append(chunk)
+
+    try:
+        async with (
+            client.aio.live.music.connect(model='models/lyria-realtime-exp') as session,
+            asyncio.TaskGroup() as tg,
+        ):
+            tg.create_task(receive_audio(session))
+
+            await session.set_weighted_prompts([
+                types.WeightedPrompt(text=prompt, weight=1.0)
+            ])
+            await session.set_music_generation_config(
+                types.LiveMusicGenerationConfig(bpm=int(bpm), temperature=float(temperature))
+            )
+
+            await session.play()
+            await asyncio.sleep(5)  # Streaming duration
+            await session.pause()
+
+        all_pcm = b"".join(audio_chunks)
+        output_path = "generated_music.wav"
+        save_wave_file(output_path, all_pcm)
+        return output_path, output_path, "Music generated successfully."
+
+    except Exception as e:
+        return None, None, f"Error: {str(e)}"
+
+# Wrapper for Gradio to run async function
+def generate_music_gradio(prompt, bpm, temperature):
+    return asyncio.run(generate_music(prompt, bpm, temperature))
+
+# Gradio UI
+with gr.Blocks(title="Gemini Lyria Music Generator") as demo:
+    gr.Markdown("## Lyria Music Generator")
+
+    # Section 1: Input
+    with gr.Group():
+        gr.Markdown("### Input")
+        with gr.Row():
+            prompt_input = gr.Textbox(
+                label="Music Style / Prompt",
+                placeholder="e.g., ambient synth, minimal techno, classical piano"
+            )
+        with gr.Row():
+            bpm_input = gr.Slider(label="BPM", minimum=60, maximum=180, value=90)
+            temp_input = gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=1.0)
+        generate_btn = gr.Button("Generate Music")
+
+    # Section 2: Output
+    with gr.Group():
+        gr.Markdown("### Output")
+        with gr.Row():
+            output_audio = gr.Audio(label="Generated Audio")
+            download_file = gr.File(label="Download WAV")
+        status_output = gr.Textbox(label="Status", interactive=False)
+
+    # Section 3: Examples
+    with gr.Group():
+        gr.Markdown("### Examples")
+        examples = gr.Examples(
+            examples=[
+                ["minimal techno", 125, 1.0],
+                ["classical piano in a rainy mood", 70, 0.9],
+                ["ambient space drone", 90, 1.5],
+                ["lo-fi chill beats", 80, 1.0],
+                ["orchestral epic music", 110, 1.2],
+            ],
+            inputs=[prompt_input, bpm_input, temp_input]
+        )
+
+    # Event binding
+    generate_btn.click(
+        fn=generate_music_gradio,
+        inputs=[prompt_input, bpm_input, temp_input],
+        outputs=[output_audio, download_file, status_output]
+    )
+
+demo.launch()