Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from transformers import pipeline
+
+# 1) Load the HF pipeline with all scores so we can show probabilities
+classifier = pipeline(
+    "text-classification",
+    model="j-hartmann/emotion-english-roberta-large",
+    return_all_scores=True
+)
+
+# 2) Wrap it in a function that returns a label→score dict
+def classify_emotion(text: str):
+    scores = classifier(text)[0]   # returns list of {label, score}
+    return {item["label"]: float(item["score"]) for item in scores}
+
+# 3) Build the Gradio interface
+iface = gr.Interface(
+    fn=classify_emotion,
+    inputs=gr.Textbox(
+        lines=2,
+        placeholder="Type any English sentence here…",
+        label="Input Text"
+    ),
+    outputs=gr.Label(
+        num_top_classes=6,
+        label="Emotion Probabilities"
+    ),
+    examples=[
+        ["I love you!"],
+        ["The movie was heart breaking!"]
+    ],
+    title="English Emotion Classifier",
+    description=(
+        "Predicts one of Ekman's 6 basic emotions plus neutral "
+        "(anger 🤬, disgust 🤢, fear 😨, joy 😀, neutral 😐, "
+        "sadness 😭, surprise 😲)."
+    )
+)
+
+if __name__ == "__main__":
+    iface.launch()