more involved

app.py

@@ -1,12 +1,25 @@
 import gradio as gr
+from transformers import pipeline
 
-# Load the model interface
-interface = gr.load("models/bhadresh-savani/distilbert-base-uncased-emotion")
+# Load the Hugging Face pipeline
+classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
 
-# Apply customizations
-interface.launch(
-    title="CMACHINES | Emotion Detection with DistilBERT",
-    description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter any text to analyze its emotional content.",
+def classify_emotion(text):
+    # Make predictions using the Hugging Face pipeline
+    predictions = classifier(text)
+    return {item["label"]: item["score"] for item in predictions}
+
+# Create a custom Gradio interface with title, description, and examples
+gr.Interface(
+    fn=classify_emotion,
+    inputs=gr.Textbox(
+        placeholder="Enter text to analyze...", 
+        label="Input Text",
+        lines=4
+    ),
+    outputs=gr.JSON(),  # Display results in JSON format
+    title="Emotion Detection with DistilBERT",
+    description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter a piece of text to analyze its emotional content!",
     examples=[
         "I am so happy to see you!",
         "I'm really angry about what happened.",
@@ -14,4 +27,4 @@ interface.launch(
         "I'm worried about the upcoming exam.",
         "Fear is the mind-killer. I will face my fear."
     ]
-)
+).launch()