Create app.py

app.py

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+import os
+import numpy as np
+import tensorflow as tf
+from fastapi import FastAPI, UploadFile, File
+from fastapi.staticfiles import StaticFiles
+from scipy import signal
+import soundfile as sf
+from datetime import datetime
+import threading, time
+
+from team_code import base_model  # ton architecture
+
+# ----------------------------
+# CONFIG
+# ----------------------------
+SIG_LEN = 32256
+N_FEATURES = 1
+MODEL_PATH = "pretrained_model.h5"
+OUTPUT_DIR = "/tmp/audio_results"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+BASE_URL = "https://heart-murmur-api.hf.space"  # adapte selon ton déploiement
+
+# ----------------------------
+# CHARGEMENT DU MODÈLE
+# ----------------------------
+print("[INFO] Chargement du modèle TensorFlow...")
+model = base_model(SIG_LEN, N_FEATURES)
+model.load_weights(MODEL_PATH)
+model.compile()  # juste pour être sûr
+print("[INFO] Modèle chargé ✅")
+
+# ----------------------------
+# FASTAPI
+# ----------------------------
+app = FastAPI(title="Heart Murmur Detection API")
+app.mount("/files", StaticFiles(directory=OUTPUT_DIR), name="files")
+
+# ----------------------------
+# FONCTION DE PRÉTRAITEMENT
+# ----------------------------
+def preprocess_audio(file_path):
+    data, sr = sf.read(file_path)
+    if data.ndim > 1:
+        data = np.mean(data, axis=1)
+    resampled = signal.resample(data, SIG_LEN)
+    return np.expand_dims(resampled, axis=(0, 2))  # (1, sig_len, 1)
+
+# ----------------------------
+# ENDPOINT PRINCIPAL
+# ----------------------------
+@app.post("/predict/")
+async def predict_murmur(audio_file: UploadFile = File(...)):
+    """
+    Upload un fichier audio (.wav, .mp3) → renvoie diagnostic + probabilité
+    """
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    tmp_path = os.path.join(OUTPUT_DIR, f"{timestamp}_{audio_file.filename}")
+    with open(tmp_path, "wb") as f:
+        f.write(await audio_file.read())
+
+    try:
+        x = preprocess_audio(tmp_path)
+        pred = float(model.predict(x)[0][0])
+        label = "Abnormal" if pred > 0.5 else "Normal"
+        prob = round(pred, 3)
+
+        report_path = os.path.join(OUTPUT_DIR, f"report_{timestamp}.txt")
+        with open(report_path, "w") as f:
+            f.write(f"Result: {label}\nProbability: {prob}\n")
+
+        return {
+            "diagnosis": label,
+            "probability": prob,
+            "rapport_url": f"{BASE_URL}/files/{os.path.basename(report_path)}",
+            "message": "✅ Analyse audio terminée."
+        }
+
+    except Exception as e:
+        return {"error": str(e)}
+
+    finally:
+        if os.path.exists(tmp_path):
+            os.remove(tmp_path)
+
+# ----------------------------
+# AUTO-CLEANUP
+# ----------------------------
+def auto_cleanup(interval_minutes=10):
+    while True:
+        time.sleep(interval_minutes * 60)
+        for file in os.listdir(OUTPUT_DIR):
+            try:
+                os.remove(os.path.join(OUTPUT_DIR, file))
+                print(f"[CLEANUP] Fichier supprimé : {file}")
+            except Exception as e:
+                print(f"[CLEANUP] Erreur : {e}")
+
+threading.Thread(target=auto_cleanup, daemon=True).start()