update

app.py

@@ -1,21 +1,39 @@
 import gradio as gr
 from transformers import pipeline
 
+# Emotion Text Classification
+title_emotion = "Classify text according to emotion"
+description_emotion = "Emotion text classification by Vishal Tiwari "
+examples_emotion = [
+    ["Remember before Twitter when you took a photo of food, got the film developed, then drove around showing everyone the pic? No? Me neither."],
+    ['''"We are all here because we're committed to the biggest question of all: What's out there?" Take your first steps toward answering that question by watching our Gameplay Reveal from the #XboxBethesda Showcase. '''],
+    ["A STUNNER IN KNOXVILLE! 😱 Notre Dame takes down No. 1 Tennessee for its first trip to Omaha in 20 years‼️"],
+    ["you and I best moment is yet to come 💜 #BTS9thAnniversary"]
+]
 
-model = pipeline(task="automatic-speech-recognition",
+interface_emotion = gr.Interface.load(
+    "huggingface/bhadresh-savani/bert-base-go-emotion",
+    title=title_emotion,
+    description=description_emotion,
+    examples=examples_emotion
+)
 
-def predict_speech_to_text(audio):
-    prediction = model(audio)
-    text = prediction['text']
-    return text
+# Text to Speech Translation
+title_tts = "Text to Speech Translation"
+examples_tts = [
+    "I love learning machine learning",
+    "How do you do?",
+]
 
-gr.Interface.load("models/facebook/s2t-medium-librispeech-asr")               
-gr.Interface(fn=predict_speech_to_text,
-             title="🧑🏽‍🎤 PROLOVE 🥰😘 ",
-             inputs=gr.inputs.Audio(
-                 source="microphone", type="filepath", label="Input"),
-             outputs=gr.outputs.Textbox(label="Output"),
-             description="This application was created to help correct pronouncation",
-             examples=['Good night_alvi.wav'],
-             allow_flagging='never'
-             ).launch()
+interface_tts = gr.Interface.load(
+    "huggingface/facebook/fastspeech2-en-ljspeech",
+    title=title_tts,
+    examples=examples_tts,
+    description="Give me something to say!",
+)
+
+# Launching both interfaces with tabs
+demo = gr.TabbedInterface([interface_emotion, interface_tts], ["Emotion Classification", "Text to Speech"])
+
+if __name__ == "__main__":
+    demo.launch()