Update app.py

app.py

@@ -1,74 +1,22 @@
-import gradio as gr
 from transformers import pipeline
 
-# Load text-to-speech model
-tts_title = "Text to Speech Translation"
-tts_examples = ["I love learning machine learning", "How do you do?"]
-tts_demo = gr.Interface.load(
-    "huggingface/facebook/fastspeech2-en-ljspeech",
-    title=tts_title,
-    examples=tts_examples,
-    description="Give me something to say!",
-)
-
-# Load emotion classification model
-emotion_model_checkpoint = "MuntasirHossain/RoBERTa-base-finetuned-emotion"
-emotion_model = pipeline("text-classification", model=emotion_model_checkpoint)
-
-def classify_emotion_and_speech(text=""):
-    # Emotion classification
-    emotion_label = emotion_model(text)[0]["label"]
-
-    # Adjust speech synthesis parameters based on emotion_label.
-    # Customize this part based on the emotion_label.
+import gradio as gr
 
-    # Replace the following line with your desired text-to-speech model and parameters.
-    speech_output = f"Emotion: {emotion_label}, Text: {text}"
+tts = pipeline("text-to-speech", "facebook/wav2vec2-base-960h")
 
-    return {"emotion_label": emotion_label, "audio": speech_output}
+def text_to_speech(text):
+    audio = tts(text)[0]["audio"]
+    return audio
 
-emotion_title = "Texts Expressing Emotion with Speech"
-emotion_description = "This AI model classifies texts expressing human emotion and converts them into speech."
-emotion_examples = [["He is very happy today", "Free Palestine"]]
+demo = gr.Blocks()
 
-# Define a theme for the Gradio interface
-theme = {
-    "container": {
-        "background-color": "#007bff",
-        "color": "#fff",
-        "padding": "20px",
-    },
-    "textbox": {
-        "background-color": "#fff",
-        "border-radius": "5px",
-        "padding": "10px",
-        "margin-bottom": "10px",
-    },
-    "button": {
-        "background-color": "#007bff",
-        "color": "#fff",
-        "padding": "10px",
-        "border-radius": "5px",
-        "cursor": "pointer",
-    },
-    "label": {
-        "color": "#fff",
-    },
-}
+with demo:
+    text_input = gr.Textbox()
+    audio_output = gr.Audio()
 
-combined_demo = gr.Interface(
-    fn=classify_emotion_and_speech,
-    inputs="textbox",
-    outputs=["text", "audio"],
-    title=emotion_title,
-    theme=theme,
-    description=emotion_description,
-    examples=emotion_examples,
-)
+    b1 = gr.Button("Convert to Speech")
 
-# Combine both demos into a Tabbed Interface
-combined_demo_tabbed = gr.TabbedInterface([tts_demo, combined_demo], ["Text to Speech", "Texts Expressing Emotion with Speech"])
+    b1.click(text_to_speech, inputs=text_input, outputs=audio_output)
 
 if __name__ == "__main__":
-    combined_demo_tabbed.launch()
-    
+    demo.launch()