use a dataframe

app.py

@@ -3,6 +3,7 @@ import gradio as gr
 from transformers import pipeline
 import logging
 import sys
+import pandas as pd
 
 # Set up logging
 logging.basicConfig(level=logging.INFO, stream=sys.stdout,
@@ -41,14 +42,14 @@ def predict_emotion(text):
         predictions = classifier(text)[0]
         logger.info(f"Raw predictions: {predictions}")
         
-        # Convert predictions to the format Gradio's BarPlot expects
-        # We need a list of tuples (emotion, score)
-        scores = [(pred['label'], float(pred['score'])) for pred in predictions]
-        logger.info(f"Processed scores: {scores}")
+        # Convert predictions to a pandas DataFrame for Gradio's BarPlot
+        df = pd.DataFrame(predictions)
+        df = df.sort_values('score', ascending=True)  # Sort for better visualization
+        logger.info(f"Processed scores as DataFrame: {df.to_dict()}")
         
         return (
-            [score[0] for score in scores],  # x values (emotions)
-            [score[1] for score in scores]   # y values (probabilities)
+            df['label'].tolist(),  # x values (emotions)
+            df['score'].tolist()   # y values (probabilities)
         )
         
     except Exception as e: