Update app.py

app.py

@@ -2,15 +2,17 @@
 import gradio as gr
 from transformers import pipeline
 
-Model_1 = "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"
-Model_2 ="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"
+#Model_1 = "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD"
+#Model_2 ="hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"
 
-def classify_sentiment(audio, model):
-  pipe = pipeline("audio-classification", model=model)
+model_name2id = {"Model A": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-classification-MESD", "Model B": "hackathon-pln-es/wav2vec2-base-finetuned-sentiment-mesd"}
+
+def classify_sentiment(audio, model_name):
+  pipe = pipeline("audio-classification", model=model_name2id[model_name])
   pred = pipe(audio)
   return {dic["label"]: dic["score"] for dic in pred}
 
-input_audio = [gr.inputs.Audio(source="microphone", type="filepath", label="Record/ Drop audio"), gr.inputs.Dropdown([Model_1, Model_2], label="Model Name")]
+input_audio = [gr.inputs.Audio(source="microphone", type="filepath", label="Record/ Drop audio"), gr.inputs.Dropdown([model_name2id[model_name], model_name2id[model_name]], label="Model Name")]
 label = gr.outputs.Label(num_top_classes=5)
 
 ################### Gradio Web APP ################################
@@ -36,6 +38,6 @@ gr.Interface(
     fn = classify_sentiment,
     inputs = input_audio,
     outputs = label,
-    examples=[["basta_neutral.wav", Model_1], ["detras_disgust.wav", Model_1], ["mortal_sadness.wav", Model_1], ["respiracion_happiness.wav", Model_1], ["robo_fear.wav", Model_1]],
+    examples=[["basta_neutral.wav", model_name2id[model_name]], ["detras_disgust.wav", model_name2id[model_name]], ["mortal_sadness.wav", model_name2id[model_name]], ["respiracion_happiness.wav", model_name2id[model_name]], ["robo_fear.wav", model_name2id[model_name]]],
     theme="grass").launch()