Create app.py

app.py

@@ -0,0 +1,47 @@
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from transformers import pipeline
+import uvicorn
+import tempfile
+
+# Initialize FastAPI
+app = FastAPI()
+
+# Enable CORS for all origins (so Render or any client can access it)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load the pretrained speech emotion recognition pipeline
+emotion_pipeline = pipeline(
+    "audio-classification",
+    model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
+)
+
+# Health check route
+@app.get("/")
+def read_root():
+    return {"message": "HF Space is live!"}
+
+# Predict route
+@app.post("/predict")
+async def predict_emotion(file: UploadFile = File(...)):
+    try:
+        # Save the uploaded audio file to a temporary location
+        with tempfile.NamedTemporaryFile(delete=False) as tmp:
+            tmp.write(await file.read())
+            tmp_path = tmp.name
+
+        # Run emotion prediction
+        result = emotion_pipeline(tmp_path)
+        top_emotion = result[0]['label']
+
+        return {"emotion": top_emotion}
+
+    except Exception as e:
+        return {"error": str(e)}