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app.py

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+import streamlit as st
+from keras.models import load_model
+import librosa
+import numpy as np
+import pickle
+
+# Model ve eğitim tarihçesini yükle
+model = load_model('my_model.h5')
+with open('pkl.pkl', 'rb') as file_pi:
+    history = pickle.load(file_pi)
+
+def detect_fake(sound_file):
+    sound_signal, sample_rate = librosa.load(sound_file, res_type="kaiser_fast")
+    mfcc_features = librosa.feature.mfcc(y=sound_signal, sr=sample_rate, n_mfcc=40)
+    mfccs_features_scaled = np.mean(mfcc_features.T, axis=0)
+    mfccs_features_scaled = mfccs_features_scaled.reshape(1, -1)
+    result_array = model.predict(mfccs_features_scaled)
+    result_classes = ["FAKE", "REAL"]
+    result = np.argmax(result_array[0])
+    return result_classes[result]
+
+# Streamlit arayüzü
+st.title('Ses Doğrulama Sistemi')
+
+uploaded_file = st.file_uploader("Ses dosyası yükle", type=["wav", "mp3", "ogg"])
+if uploaded_file is not None:
+    # Dosyayı kaydet
+    with open(uploaded_file.name, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    result = detect_fake(uploaded_file.name)
+    st.write(f"Tahmin: {result}")

requirements.txt

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+streamlit
+keras
+librosa
+numpy
+pickle5