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app.py

@@ -1,21 +1,13 @@
 import gradio as gr
 from transformers import pipeline
-import pandas as pd
 
 # Load the Hugging Face pipeline
-classifier = pipeline(
-    "text-classification", 
-    model="bhadresh-savani/distilbert-base-uncased-emotion"
-)
+classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion")
 
 # Define the full list of possible emotions (based on the model output structure)
 ALL_EMOTIONS = ["sadness", "joy", "love", "anger", "fear", "surprise"]
 
 def classify_emotion(text):
-    # Check if input text is valid
-    if not text or not text.strip():
-        raise ValueError("Input text cannot be empty.")  # Raise an error for invalid input
-    
     # Make predictions using the Hugging Face pipeline
     predictions = classifier(text)
     
@@ -26,18 +18,7 @@ def classify_emotion(text):
     for item in predictions:
         emotion_scores[item["label"]] = item["score"]
     
-    # Create a dataframe for the bar plot
-    df = pd.DataFrame({
-        "Emotion": list(emotion_scores.keys()),
-        "Score": list(emotion_scores.values())
-    })
-    
-    # Prepare a text-based table for display
-    table = "Emotion Scores:\n"
-    table += "----------------\n"
-    table += "\n".join([f"{emotion}: {score:.4f}" for emotion, score in emotion_scores.items()])
-    
-    return df, table
+    return emotion_scores
 
 # Create a custom Gradio interface with title, description, and examples
 gr.Interface(
@@ -47,22 +28,11 @@ gr.Interface(
         label="Input Text",
         lines=4
     ),
-    outputs=[
-        gr.BarPlot(
-            x="Emotion",  # Specify the x-axis column
-            y="Score",    # Specify the y-axis column
-            label="Emotion Scores Bar Plot",
-            title="Emotion Probabilities",
-            color="#2563eb",  # Set the bar color
-            height=400,
-            vertical=True
-        ),
-        gr.Textbox(label="Emotion Scores Table")  # Text-based table output
-    ],
-    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! Both a bar plot and a text table of the scores will be displayed.",
+    outputs=gr.Label(label="Emotion Probabilities"),  # Use gr.Label for a cleaner interface
+    title="CMACHINES25 | 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 am not so happy to see you!",
         "I'm really angry about what happened.",
         "The sunset was absolutely beautiful today.",
         "I'm worried about the upcoming exam.",