initial

app.py

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+# app.py
+import gradio as gr
+from transformers import pipeline
+
+# Initialize the emotion classification pipeline
+classifier = pipeline("text-classification", 
+                     model="bhadresh-savani/distilbert-base-uncased-emotion",
+                     return_all_scores=True)
+
+def predict_emotion(text):
+    if not text:
+        return {
+            "sadness": 0,
+            "joy": 0,
+            "love": 0,
+            "anger": 0,
+            "fear": 0,
+            "surprise": 0
+        }
+    
+    # Get predictions
+    predictions = classifier(text)[0]
+    
+    # Convert to dictionary format for the bar chart
+    scores = {pred['label']: pred['score'] for pred in predictions}
+    return scores
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=predict_emotion,
+    inputs=gr.Textbox(placeholder="Enter text to analyze...", label="Input Text"),
+    outputs=gr.BarPlot(
+        x="emotion",
+        y="probability",
+        title="Emotion Probabilities",
+        tooltip=["emotion", "probability"],
+        height=400
+    ),
+    title="Emotion Detection with DistilBERT",
+    description="This app uses the DistilBERT model fine-tuned for emotion detection. Enter any text to analyze its emotional content.",
+    examples=[
+        ["I am 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."]
+    ],
+    allow_flagging="never"
+)
+
+demo.launch()