Update app.py

app.py

@@ -1,4 +1,4 @@
-# main.py
+# app.py
 import os
 import tempfile
 import base64
@@ -10,10 +10,16 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
 import uvicorn
 
+# Hugging Face Spaces specific setup
+os.environ["HF_HOME"] = "/tmp/hf_cache"
+os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/hf_cache"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/hf_cache"
+
 from rhythma import RhythmaSymphAICore, RhythmaModulationEngine
 
-app = FastAPI(title="Rhythma API")
+app = FastAPI(title="Rhythma: The Living Modulation Engine")
 
+# Enable CORS for the frontend
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -22,9 +28,10 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Serve the beautiful frontend
+# Mount static files so index.html is accessible
 app.mount("/static", StaticFiles(directory="."), name="static")
 
+# Initialize the core components
 symphai = RhythmaSymphAICore(use_groq=True)
 
 @app.post("/generate")
@@ -38,16 +45,19 @@ async def generate(
 ):
     audio_path = None
     if audio and audio.filename:
+        # Save uploaded audio to temp file
         with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:
             tmp.write(await audio.read())
             audio_path = tmp.name
 
     try:
+        # Step 1: Analyze input using your SymphAI Core
         analysis = symphai.analyze_input(
             input_text.strip() or None,
             audio_path
         )
 
+        # Step 2: Generate modulated experience
         engine = RhythmaModulationEngine(
             base_freq=override_freq if override_freq > 0 else None,
             modulation_type=override_modulation,
@@ -55,30 +65,29 @@ async def generate(
             emotional_state=analysis.get("emotional_state")
         )
 
-        # Audio
+        # Generate audio file
         timestamp = int(os.times()[4] * 1000)
         audio_file = f"rhythma_{timestamp}.wav"
         saved_audio_path = engine.save_audio(duration, audio_file)
 
-        # Waveform image (PIL)
+        # Generate visualizations
         waveform_pil = engine.get_waveform_image()
-
-        # Full plot
         fig = engine.visualize_waveform(duration)
 
-        # Convert plot to base64
+        # Convert matplotlib figure to base64
         buf = io.BytesIO()
         fig.savefig(buf, format="png", bbox_inches="tight", dpi=220)
         buf.seek(0)
         plot_base64 = base64.b64encode(buf.read()).decode("utf-8")
         plt.close(fig)
 
-        # Convert simple waveform to base64
+        # Convert simple waveform PIL image to base64
         buf = io.BytesIO()
         waveform_pil.save(buf, format="PNG")
         buf.seek(0)
         simple_wave_base64 = base64.b64encode(buf.read()).decode("utf-8")
 
+        # Return all data to the frontend
         return {
             "analysis_text": engine.get_complete_analysis(),
             "audio_base64": base64.b64encode(open(saved_audio_path, "rb").read()).decode("utf-8"),
@@ -96,15 +105,11 @@ async def generate(
         return JSONResponse(status_code=500, content={"error": str(e)})
 
     finally:
+        # Clean up temporary audio file
         if audio_path and os.path.exists(audio_path):
             os.unlink(audio_path)
 
+
+# For local testing only (Hugging Face uses the Dockerfile CMD)
 if __name__ == "__main__":
-    print("🚀 Rhythma is live → http://localhost:8000/static/index.html")
-    print("   (Press CTRL+C to stop)")
-    uvicorn.run(
-        "app:app",           # ← This was the problem (was "main:app")
-        host="0.0.0.0",
-        port=8000,
-        reload=True
-    )
+    uvicorn.run("app:app", host="0.0.0.0", port=7860)