Delete app2.py

app2.py

@@ -1,34 +0,0 @@
-import tensorflow as tf
-import joblib
-import numpy as np
-import gradio as gr
-import os
-
-# Load model with correct filename
-model = tf.keras.models.load_model("animal_sound_cnn.keras")
-
-# Load label encoder
-label_encoder = joblib.load("label_encoder.joblib")
-
-def predict(audio_path):
-    try:
-        # 1. Preprocess audio (replace this with your actual preprocessing)
-        # Example placeholder - you'll need to implement your audio feature extraction
-        spectrogram = np.random.rand(1, 64, 64, 1)  # Replace with real preprocessing
-        
-        # 2. Predict
-        pred = model.predict(spectrogram)
-        animal = label_encoder.inverse_transform([np.argmax(pred)])[0]
-        
-        return animal
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Gradio interface
-gr.Interface(
-    fn=predict,
-    inputs=gr.Audio(type="filepath"),
-    outputs="label",
-    title="Animal Sound Classifier",
-    description="Upload an animal sound to identify the species"
-).launch()