Update app.py

app.py

@@ -1,7 +1,49 @@
 import gradio as gr
+import torch
+import soundfile as sf
+import os
+import matplotlib.pyplot as plt
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+import os
+import soundfile as sf
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
+from sklearn.model_selection import train_test_split
+import re
+from collections import Counter
+from sklearn.metrics import classification_report
+model = Wav2Vec2ForSequenceClassification.from_pretrained("facebook/wav2vec2-base-960h", num_labels=2).to(device)
+model_path = "dysarthria_classifier12.pth"
+if os.path.exists(model_path):
+    print(f"Loading saved model {model_path}")
+    model.load_state_dict(torch.load(model_path))
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+def predict(file_path):
+    max_length = 100000
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    model.eval()
+    with torch.no_grad():
+        wav_data, _ = sf.read(file_path.name)
+        inputs = processor(wav_data, sampling_rate=16000, return_tensors="pt", padding=True)
+
+        input_values = inputs.input_values.squeeze(0)  
+        if max_length - input_values.shape[-1] > 0:
+            input_values = torch.cat([input_values, torch.zeros((max_length - input_values.shape[-1],))], dim=-1)
+        else:
+            input_values = input_values[:max_length]
+        input_values = input_values.unsqueeze(0).to(device)
+        inputs = {"input_values": input_values}
+
+        logits = model(**inputs).logits
+        logits = logits.squeeze()
+        predicted_class_id = torch.argmax(logits, dim=-1).item()
+
+    return predicted_class_id
+
+iface = gr.Interface(fn=predict, inputs="file", outputs="text")
+iface.launch()