app.py

import gradio as gr
import numpy as np
from PIL import Image
from app.preprocess import preprocess_audio
from app.model import predict
from collections import Counter, defaultdict
import librosa
import random
from datasets import load_dataset

# Load Hugging Face datasets
audio_ds = load_dataset("AIOmarRehan/General_Audio_Dataset", split="train")
image_ds = load_dataset("AIOmarRehan/Mel_Spectrogram_Images_for_Audio_Classification", split="train")


# IMAGE HANDLING
def safe_load_image(img):
    if img is None:
        return None
    if isinstance(img, np.ndarray):
        img = Image.fromarray(img)
    img = img.convert("RGBA")
    return img


# PROCESS SPECTROGRAM IMAGE
def process_image_input(img):
    img = safe_load_image(img)
    label, confidence, probs = predict(img)
    return label, round(confidence, 3), probs


# PROCESS RAW AUDIO
def process_audio_input(audio_path):
    imgs = preprocess_audio(audio_path)
    all_preds, all_confs, all_probs = [], [], []

    for img in imgs:
        label, conf, probs = predict(img)
        all_preds.append(label)
        all_confs.append(conf)
        all_probs.append(probs)

    # Majority vote
    counter = Counter(all_preds)
    max_count = max(counter.values())
    candidates = [k for k, v in counter.items() if v == max_count]

    if len(candidates) == 1:
        final_label = candidates[0]
    else:
        conf_sums = defaultdict(float)
        for i, lbl in enumerate(all_preds):
            if lbl in candidates:
                conf_sums[lbl] += all_confs[i]
        final_label = max(conf_sums, key=conf_sums.get)

    final_conf = float(np.mean([all_confs[i] for i, lbl in enumerate(all_preds) if lbl == final_label]))

    return final_label, round(final_conf, 3), all_preds, [round(c, 3) for c in all_confs]


# MAIN CLASSIFIER
def classify(audio_path, image, random_audio, random_image):
    # Load random audio if selected
    if random_audio:
        rand_sample = random.choice(audio_ds)
        audio_path = rand_sample["audio"]["path"]

    # Load random image if selected
    if random_image:
        rand_sample = random.choice(image_ds)
        img_bytes = rand_sample["image"]
        image = Image.open(img_bytes).convert("RGBA")

    # If spectrogram image
    if image is not None:
        label, conf, probs = process_image_input(image)
        return {
            "Final Label": label,
            "Confidence": conf,
            "Details": probs
        }, label  

    # If raw audio
    if audio_path is not None:
        label, conf, all_preds, all_confs = process_audio_input(audio_path)
        return {
            "Final Label": label,
            "Confidence": conf,
            "All Chunk Labels": all_preds,
            "All Chunk Confidences": all_confs
        }, label  

    return "Please upload an audio file OR a spectrogram image.", ""


# GRADIO UI
interface = gr.Interface(
    fn=classify,
    inputs=[
        gr.Audio(type="filepath", label="Upload Audio (WAV/MP3)"),
        gr.Image(type="pil", label="Upload Spectrogram Image (PNG RGBA Supported)"),
        gr.Checkbox(label="Pick Random Audio from Dataset"),
        gr.Checkbox(label="Pick Random Image from Dataset"),
    ],
    outputs=[
        gr.JSON(label="Prediction Results"),
        gr.Textbox(label="Final Label", interactive=False)
    ],
    title="General Audio Classifier (Audio + Spectrogram Support)",
    description=(
        "Upload a raw audio file OR a spectrogram image.\n"
        "You can also select random samples from the Hugging Face datasets.\n"
        "The output shows a JSON with all details and a separate field for the final label."
    ),
)

interface.launch()