added sample app and requirements

app.py

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+# Import required libraries
+import gradio as gr # For interface
+from sentence_transformers import SentenceTransformer # For embedding the text
+import torch # For gpu 
+
+# Make the app device agnostic
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+# Load a pretrained Sentence Transformer model
+model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
+
+# Function that does the embedding
+def predict(input_text):
+    
+    # Calculate embeddings by calling model.encode()
+    embeddings = model.encode(input_text)
+
+    return embeddings
+
+# Gradio app interface
+gradio_app = gr.Interface(
+    predict,
+    inputs="text", 
+    outputs="text",
+    title="Embeddings"
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()

requirements.txt

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+transformers
+torch
+sentence-transformers