Update app.py

app.py

@@ -1,47 +1,48 @@
 import gradio as gr
 from transformers import pipeline
 
-model_checkpoint = "MuntasirHossain/RoBERTa-base-finetuned-emotion"
-emotion_model = pipeline("text-classification", model=model_checkpoint)
-
-def classify_emotion(text):
-    label = emotion_model(text)[0]["label"]
-    return label
-
-description = "This AI model is trained to classify texts expressing human emotion into different categories."
-title = "Texts Expressing Emotion"
-examples = [["He is very happy today", "Free Palestine"]]
-
-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",
-    },
-}
-
-gr.Interface(
-    fn=classify_emotion,
+# 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.
+
+    # Replace the following line with your desired text-to-speech model and parameters.
+    speech_output = f"Emotion: {emotion_label}, Text: {text}"
+
+    return {"emotion_label": emotion_label, "audio": speech_output}
+
+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"]]
+
+combined_demo = gr.Interface(
+    fn=classify_emotion_and_speech,
     inputs="textbox",
-    outputs="text",
-    title=title,
+    outputs=["text", "audio"],
+    title=emotion_title,
     theme=theme,
-    description=description,
-    examples=examples,
-).launch()
+    description=emotion_description,
+    examples=emotion_examples,
+)
+
+# Combine both demos into a Tabbed Interface
+combined_demo_tabbed = gr.TabbedInterface([tts_demo, combined_demo], ["Text to Speech", "Texts Expressing Emotion with Speech"])
+
+if __name__ == "__main__":
+    combined_demo_tabbed.launch()