Create app.py

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model for emotion detection
+classifier = pipeline(
+    "text-classification",
+    model='bhadresh-savani/distilbert-base-uncased-emotion',
+    return_all_scores=True
+)
+
+def detect_emotions(emotion_input):
+    """
+    Detect emotions in the input text using a pre-trained model.
+    Returns a dictionary mapping emotions to their respective scores.
+    """
+    prediction = classifier(emotion_input)
+    output = {emotion["label"]: round(emotion["score"], 4) for emotion in prediction[0]}
+    return output
+
+examples = [
+    ["Every song on the radio reminds me of you."],
+    ["There's an unfamiliar shadow in the corner of the room."]
+]
+
+css = """
+footer {display: none !important;}
+.output-markdown {display: none !important;}
+.gr-button-primary {
+    z-index: 14;
+    height: 43px;
+    width: 130px;
+    left: 0px;
+    top: 0px;
+    padding: 0px;
+    cursor: pointer !important;
+    background: rgb(17, 20, 45) !important;
+    border: none !important;
+    text-align: center !important;
+    font-family: 'Poppins', sans-serif !important;
+    font-size: 14px !important;
+    font-weight: 500 !important;
+    color: rgb(255, 255, 255) !important;
+    line-height: 1 !important;
+    border-radius: 12px !important;
+    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
+    box-shadow: none !important;
+}
+.gr-button-primary:hover {
+    background: rgb(66, 133, 244) !important;
+    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
+}
+"""
+
+interface = gr.Interface(
+    fn=detect_emotions,
+    inputs=gr.Textbox(placeholder="Enter text here", label="Input", lines=2),
+    outputs=gr.Label(num_top_classes=5, label="Emotion"),
+    title="Emotion Analysis",
+    description="Enter a text to detect the underlying emotions using a DistilBERT-based model.",
+    examples=examples,
+    css=css
+)
+
+if __name__ == "__main__":
+    interface.launch()