Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("padmajabfrl/Gender-Classification")
+model = AutoModelForSequenceClassification.from_pretrained("padmajabfrl/Gender-Classification")
+
+# Function to predict gender
+def predict_gender(name):
+    inputs = tokenizer(name, return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = outputs.logits.argmax(dim=-1)
+    predicted_label = model.config.id2label[predictions.item()]
+    return predicted_label
+
+# Create a Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>Kaleida Gender Prediction Transformer</h1>")
+    gr.Markdown("<h3 style='text-align: center;'>Tops Infosolution 🤝 Kaleida</h3>")
+    
+    with gr.Row():
+        with gr.Column():
+            name_input = gr.Textbox(label="Enter a Name", placeholder="Type a name here...", lines=1)
+            classify_button = gr.Button("Predict Gender")
+        
+        with gr.Column():
+            output_label = gr.Label(label="Predicted Gender")
+    
+    classify_button.click(predict_gender, inputs=name_input, outputs=output_label)
+
+# Launch the app
+demo.launch(auth=("kaleida", "kaleida@1234"))