Create app.py

app.py

@@ -0,0 +1,35 @@
+import joblib
+import librosa
+import numpy as np
+import gradio as gr
+
+# --- Load the trained Random Forest model ---
+MODEL_PATH = "model.joblib"
+model = joblib.load(MODEL_PATH)
+
+# --- Extract MFCC features from audio ---
+def extract_mfcc(file_path):
+    y, sr = librosa.load(file_path, sr=None)
+    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    return np.mean(mfcc, axis=1)
+
+# --- Prediction function ---
+def predict_audio(audio_file):
+    try:
+        features = extract_mfcc(audio_file).reshape(1, -1)
+        prediction = model.predict(features)[0]
+        return "True Story" if prediction == 1 else "Deceptive Story"
+    except Exception as e:
+        return f"Error: {e}"
+
+# --- Gradio Interface ---
+iface = gr.Interface(
+    fn=predict_audio,
+    inputs=gr.Audio(type="filepath", label="Upload Audio File"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="Truth Detection Model",
+    description="Upload an audio clip to detect if the story is true or deceptive."
+)
+
+if __name__ == "__main__":
+    iface.launch()