Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from datetime import datetime
+import numpy as np
+import torch, librosa, soundfile as sf
+
+# --------- INIT MODELS ----------
+musicgen = pipeline("text-to-audio", model="facebook/musicgen-small")
+sentiment = pipeline("sentiment-analysis")
+
+# --------- UTILITIES ----------
+def detect_mood(text, mic_audio=None):
+    """Combine user text + voice tone → mood keyword."""
+    if mic_audio is not None:
+        # rough energy check from mic input
+        y, sr = librosa.load(mic_audio, sr=16000)
+        rms = np.mean(librosa.feature.rms(y=y))
+        energy = "calm" if rms < 0.01 else "energetic"
+    else:
+        energy = "neutral"
+
+    base = sentiment(text)[0]["label"].lower()
+    if "neg" in base:
+        mood = "melancholic"
+    elif "pos" in base:
+        mood = "uplifting"
+    else:
+        mood = "reflective"
+
+    if energy == "energetic" and mood == "uplifting":
+        mood = "joyous"
+    return mood
+
+# --------- CORE GENERATION ----------
+def generate_music(prompt, mic_input=None):
+    mood = detect_mood(prompt, mic_input)
+    hour = datetime.now().hour
+    time_tag = (
+        "morning calm"
+        if 5 <= hour < 12
+        else "evening glow"
+        if 17 <= hour < 22
+        else "midnight dream"
+    )
+
+    full_prompt = f"{prompt}, {mood} atmosphere, {time_tag} tone"
+    print(f"[EchoBloom] prompt → {full_prompt}")
+
+    audio_out = musicgen(full_prompt, forward_params={"do_sample": True, "max_new_tokens": 256})[0]
+    sf.write("out.wav", audio_out["audio"], audio_out["sampling_rate"])
+    return "out.wav", f"Mood: {mood} | Scene: {time_tag}"
+
+# --------- GRADIO UI ----------
+with gr.Blocks(title="EchoBloom: Adaptive Listener Companion") as demo:
+    gr.Markdown("## 🌱 EchoBloom — Adaptive Listener\nWhisper a mood, hum a phrase, or let silence bloom into sound.")
+
+    with gr.Row():
+        text_in = gr.Textbox(label="Your Whisper / Mood Prompt", placeholder="e.g., 'Drifting through starlit fields'")
+        mic_in = gr.Audio(source="microphone", type="filepath", label="Voice Tone (optional)")
+
+    generate_btn = gr.Button("🌸 Bloom Soundscape")
+    audio_out = gr.Audio(label="Generated Music", type="filepath")
+    mood_info = gr.Textbox(label="Mood State", interactive=False)
+
+    generate_btn.click(fn=generate_music, inputs=[text_in, mic_in], outputs=[audio_out, mood_info])
+
+    gr.Markdown(
+        "Tip: Try whispering something happy vs. calm, and watch how the soundtrack changes with your voice energy!"
+    )
+
+if __name__ == "__main__":
+    demo.launch()