Update app.py

app.py

@@ -1,46 +1,47 @@
 import gradio as gr
-import nltk
-
-# Download the VADER lexicon
-nltk.download('vader_lexicon')
-
-# Rest of your code
-from nltk.sentiment import SentimentIntensityAnalyzer
-
-# Text to Speech
-title = "Text to Speech with Sentiment Analysis"
-tts_examples = [
-    "I love learning machine learning",
-    "How do you do?",
-]
-
-def get_sentiment(text):
-    # Get sentiment score
-    sia = SentimentIntensityAnalyzer()
-    sentiment_score = sia.polarity_scores(text)["compound"]
-
-    # Adjust speech synthesis parameters based on sentiment
-    # You can customize this part based on the sentiment score.
-
-    # For example, if sentiment is positive, use a happy tone; if negative, use a sad tone.
-
-    # Modify the speech synthesis model and parameters accordingly.
-    # Use the sentiment_score to adjust the tone.
-
-    # Replace the following line with your desired text-to-speech model and parameters.
-    speech_output = f"This is a text with sentiment score {sentiment_score}: {text}"
-
-    return {"audio": speech_output}
-
-tts_demo = gr.Interface(
-    fn=get_sentiment,
-    inputs="text",
-    outputs="audio",
-    examples=tts_examples,
+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,
+    inputs="textbox",
+    outputs="text",
     title=title,
-    description="Give me something to say with sentiment analysis!",
-)
-
-demo = gr.TabbedInterface([tts_demo], ["Text to speech with sentiment"])
-if __name__ == "__main__":
-    demo.launch()
+    theme=theme,
+    description=description,
+    examples=examples,
+).launch()