Update app.py

app.py

@@ -1,82 +1,46 @@
 # app.py
 
 import gradio as gr
-import torch
 import numpy as np
-import librosa
-from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification
 
-# ------------------------------------------------
-# 1. Load base Wav2Vec2 model + classification head
-# ------------------------------------------------
-model_name = "facebook/wav2vec2-base-960h"
-
-# Specify num_labels=8 to create a random classification head on top.
-model = Wav2Vec2ForSequenceClassification.from_pretrained(
-    model_name,
-    num_labels=8
-)
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
-
-model.eval()
-
-# ------------------------------------------------
-# 2. Define inference function
-# ------------------------------------------------
-def classify_accuracy(audio):
+def reverse_audio(audio):
     """
-    Receives a tuple (sample_rate, data) from Gradio when type='numpy'.
-    Resamples if needed, runs a forward pass, and returns a 'level'.
+    Reverses the input audio data.
+    'audio' is a tuple (sample_rate, data) when type='numpy'.
     """
     if audio is None:
-        return "No audio provided."
+        return None  # No audio provided
 
     sample_rate, data = audio
-
-    # Ensure data is a NumPy array.
-    if not isinstance(data, np.ndarray):
-        data = np.array(data)
-
-    # Resample to 16kHz if needed.
-    target_sr = 16000
-    if sample_rate != target_sr:
-        data = librosa.resample(data, orig_sr=sample_rate, target_sr=target_sr)
-        sample_rate = target_sr
-
-    # Extract features from the audio data.
-    inputs = feature_extractor(
-        data,
-        sampling_rate=sample_rate,
-        return_tensors="pt",
-        padding=True
-    )
-
-    # Run model inference.
-    with torch.no_grad():
-        outputs = model(**inputs)
-        logits = outputs.logits
-        predicted_id = torch.argmax(logits, dim=-1).item()
-
-    # Map predicted id (0..7) to the final level (3..10).
-    accuracy_level = predicted_id + 3
-    return f"Predicted Accuracy Level: {accuracy_level}"
-
-# ------------------------------------------------
-# 3. Build Gradio interface
-# ------------------------------------------------
-title = "Speech Accuracy Classifier (Base Wav2Vec2)"
-description = (
-    "Record audio using your microphone or upload an audio file (left). "
-    "The model (not fine-tuned) will classify the audio into an accuracy level (right)."
-)
-
-# Using source="microphone" allows for direct recording, while recent versions also enable file upload.
+    # Convert to NumPy array if not already
+    data = np.array(data)
+    
+    # Reverse the audio samples
+    reversed_data = np.flipud(data)
+    
+    # Return (sample_rate, reversed_data) so Gradio can play it back
+    return (sample_rate, reversed_data)
+
+# Build the Gradio interface
 demo = gr.Interface(
-    fn=classify_accuracy,
-    inputs=gr.Audio(source="microphone", type="numpy", label="Record/Upload Audio"),
-    outputs=gr.Textbox(label="Classification Result"),
-    title=title,
-    description=description,
+    fn=reverse_audio,
+    inputs=gr.Audio(
+        source="microphone",   # enables microphone recording (Gradio 3.2+)
+        type="numpy",          # returns (sample_rate, data) to the function
+        label="Record/Upload Audio",
+        show_label=False,      # optionally hide the label
+        bg_color="#FFFFFF",    # optional background color
+        elem_id="audio_input", # optional element ID for custom styling
+        # Additional arguments you can customize:
+        # interactive=True,
+        # show_share_button=False,
+    ),
+    outputs="audio",          # return reversed audio for playback
+    title="Microphone Audio Reverse Demo",
+    description=(
+        "Records audio from your microphone or lets you upload an audio file. "
+        "Then the audio is reversed and played back."
+    ),
     allow_flagging="never"
 )