| 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 |
|
|
| |
| audio_ds = load_dataset("AIOmarRehan/General_Audio_Dataset", split="train") |
| image_ds = load_dataset("AIOmarRehan/Mel_Spectrogram_Images_for_Audio_Classification", split="train") |
|
|
|
|
| |
| 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 |
|
|
|
|
| |
| def process_image_input(img): |
| img = safe_load_image(img) |
| label, confidence, probs = predict(img) |
| return label, round(confidence, 3), probs |
|
|
|
|
| |
| 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) |
|
|
| |
| 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] |
|
|
|
|
| |
| def classify(audio_path, image, random_audio, random_image): |
| |
| if random_audio: |
| rand_sample = random.choice(audio_ds) |
| audio_path = rand_sample["audio"]["path"] |
|
|
| |
| if random_image: |
| rand_sample = random.choice(image_ds) |
| img_bytes = rand_sample["image"] |
| image = Image.open(img_bytes).convert("RGBA") |
|
|
| |
| if image is not None: |
| label, conf, probs = process_image_input(image) |
| return { |
| "Final Label": label, |
| "Confidence": conf, |
| "Details": probs |
| }, label |
|
|
| |
| 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.", "" |
|
|
|
|
| |
| 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() |
