Update app.py

app.py

@@ -1,25 +1,42 @@
 from transformers import pipeline
 import gradio as gr
+import emoji
 
+# Load pre-trained emotion classification model
 model_checkpoint = "MuntasirHossain/RoBERTa-base-finetuned-emotion"
+emotion_model = pipeline("text-classification", model=model_checkpoint)
 
-model = pipeline("text-classification", model=model_checkpoint)
+def classify_and_visualize_emotion(text):
+    # Get the predicted emotion label
+    emotion_label = emotion_model(text)[0]["label"]
+    
+    # Map emotion labels to corresponding emojis
+    emoji_mapping = {
+        "joy": "😄",
+        "sadness": "😢",
+        "love": "❤️",
+        "anger": "😡",
+        "fear": "😱",
+        "surprise": "😲",
+    }
+    
+    # Get the corresponding emoji for the predicted emotion
+    predicted_emoji = emoji_mapping.get(emotion_label, "🤔")
 
+    # Construct the result string with the predicted emotion and emoji
+    result_text = f"Predicted Emotion: {emotion_label.capitalize()} {predicted_emoji}"
 
-def classify(text):
-    label = model(text)[0]["label"]
-    return label
+    return result_text
 
-description = "This AI model is trained to classify texts expressing human emotion into six categories: sadness, joy, love, anger, fear, and surprise." 
-title = "Classify Texts Expressing Emotion"
-# theme = "peach"
-examples=[["This is such a beautiful place"]]
+# Define interface title and description
+title = "Emotion Classifier with Emoji Visualization"
+description = "This AI model classifies text expressions into six emotions: joy, sadness, love, anger, fear, and surprise. The result is visualized with emojis."
 
-gr.Interface(fn=classify,
+# Set up Gradio Interface
+gr.Interface(
+    fn=classify_and_visualize_emotion,
     inputs="textbox",
-    outputs="text",
+    outputs="html",
     title=title,
-    # theme = theme,
     description=description,
-    examples=examples,
-).launch()
+).launch()