Upload Old app.py

Old app.py

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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()