Update app.py

app.py

@@ -1,60 +1,3 @@
 import gradio as gr
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchaudio
-from transformers import AutoConfig, Wav2Vec2Processor, Wav2Vec2FeatureExtractor
-from src.models import Wav2Vec2ForSpeechClassification
 
-import librosa
-import IPython.display as ipd
-import numpy as np
-import pandas as pd
-import os
-
-model_name_or_path = "andromeda01111/Malayalam_SA"
-config = AutoConfig.from_pretrained(model_name_or_path)
-feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name_or_path)
-sampling_rate = feature_extractor.sampling_rate
-model = Wav2Vec2ForSpeechClassification.from_pretrained(model_name_or_path)
-
-def speech_file_to_array_fn(path, sampling_rate):
-    speech_array, _sampling_rate = torchaudio.load(path)
-    resampler = torchaudio.transforms.Resample(_sampling_rate)
-    speech = resampler(speech_array).squeeze().numpy()
-    return speech
-
-def predict(path, sampling_rate):
-    speech = speech_file_to_array_fn(path, sampling_rate)
-    features = feature_extractor(speech, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
-
-    input_values = features.input_values
-    attention_mask = features.attention_mask
-
-    with torch.no_grad():
-        logits = model(input_values, attention_mask=attention_mask).logits
-
-    scores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]
-    outputs = [{"Emotion": config.id2label[i], "Score": f"{round(score * 100, 3):.1f}%"} for i, score in enumerate(scores)]
-
-    return outputs
-
-# Wrapper function for Gradio
-def gradio_predict(audio):
-    predictions = predict(audio)
-    return [f"{pred['Emotion']}: {pred['Score']}" for pred in predictions]
-
-# Gradio interface
-emotions = [config.id2label[i] for i in range(len(config.id2label))]
-outputs = [gr.Textbox(label=emotion, interactive=False) for emotion in emotions]
-
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Audio(label="Upload Audio", type="filepath"),
-    outputs=outputs,
-    title="Emotion Recognition",
-    description="Upload an audio file to predict emotions and their corresponding percentages.",
-)
-
-# Launch the app
-interface.launch()
+gr.Interface.load("models/andromeda01111/Malayalam_SA").launch()