Create inference.py

inference.py

@@ -0,0 +1,41 @@
+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)
+
+# Function to extract MFCC features
+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 during prediction: {e}"
+
+# Gradio Blocks layout
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Truth Detection from Audio Stories</h1>")
+    gr.Markdown(
+        "<p style='text-align: center;'>"
+        "This tool analyzes an audio story and predicts whether it is true or deceptive "
+        "based on MFCC features and a trained Random Forest classifier."
+        "</p>"
+    )
+    audio_input = gr.Audio(type="filepath", label="Upload Audio File")
+    output = gr.Textbox(label="Prediction")
+    submit_btn = gr.Button("Predict")
+    submit_btn.click(fn=predict_audio, inputs=audio_input, outputs=output)
+    gr.Markdown("<p style='text-align: center; font-size: 12px; color: gray;'>Developed by Sangam Sanjay Bhamare, 2025.</p>")
+
+if __name__ == "__main__":
+    demo.launch()