finally

app.py

@@ -1,3 +1,77 @@
 import gradio as gr
+import torch
+import torchaudio
+import numpy as np
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+import torch.nn.functional as F
+import torchaudio.transforms as T
 
-gr.load("models/Zeyadd-Mostaffa/Deepfake-Audio-Detection-v1").launch()
+MODEL_ID = "Zeyadd-Mostaffa/Deepfake-Audio-Detection-v1"
+
+# 1) Load model & feature extractor
+feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
+model = AutoModelForAudioClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+# Optionally use GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+label_names = ["fake", "real"]  # According to your label2id = {"fake": 0, "real": 1}
+
+def classify_audio(audio_file):
+    """
+    audio_file: path to the uploaded file (WAV, MP3, etc.)
+    Returns: predicted label and confidence score
+    """
+
+    # 2) Load the audio file
+    waveform, sr = torchaudio.load(audio_file)
+
+    # If stereo, pick one channel or average
+    if waveform.shape[0] > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    waveform = waveform.squeeze()  # (samples,)
+
+    # 3) Resample if needed
+    if sr != 16000:
+        resampler = T.Resample(sr, 16000)
+        waveform = resampler(waveform)
+        sr = 16000
+
+    # 3) Preprocess with feature_extractor
+    inputs = feature_extractor(
+        waveform.numpy(),
+        sampling_rate=sr,
+        return_tensors="pt",
+        truncation=True,
+        max_length=int(16000 * 6.0),  # 6 second max
+    )
+
+    # Move everything to device
+    input_values = inputs["input_values"].to(device)
+
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+        # 4) Calculate probabilities using softmax
+        probabilities = F.softmax(logits, dim=-1)
+        
+        # Get predicted label and confidence
+        confidence, pred_id = torch.max(probabilities, dim=-1)
+        predicted_label = label_names[pred_id.item()]
+
+    # 5) Return label and confidence percentage
+    return f"Prediction: {predicted_label}, Confidence: {confidence.item() * 100:.2f}%"
+
+# 6) Build Gradio interface
+demo = gr.Interface(
+    fn=classify_audio,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="Wav2Vec2 Deepfake Detection",
+    description="Upload an audio sample to check if it is fake or real, along with confidence."
+)
+
+if __name__ == "__main__":
+    demo.launch()