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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
# IMAGE HANDLING
def safe_load_image(img):
"""
Ensure the input is a valid PIL RGBA image.
Gradio sometimes gives numpy arrays → we convert safely.
"""
if img is None:
return None
# If numpy array → convert to PIL
if isinstance(img, np.ndarray):
img = Image.fromarray(img)
# Convert to RGBA, to make sure the Alpha channel keep
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) # returns list of PIL RGBA images
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, label in enumerate(all_preds):
if label in candidates:
conf_sums[label] += 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):
# If spectrogram image
if image is not None:
label, conf, probs = process_image_input(image)
return {
"Final Label": label,
"Confidence": conf,
"Details": probs
}
# 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
}
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)")
],
outputs=gr.JSON(label="Prediction Results"),
title="General Audio Classifier (Audio + Spectrogram Support)",
description=(
"Upload a raw audio file OR a spectrogram image.\n"
"If audio → model preprocesses into mel-spectrogram chunks.\n"
"If image → model classifies the spectrogram directly.\n"
),
)
interface.launch() |