Create app.py

app.py

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+from sentence_transformers import SentenceTransformer
+import gradio as gr
+
+# Load the pre-trained model
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Define the function to process requests
+def generate_embeddings(chunks):
+    embeddings = embedding_model.encode(chunks, convert_to_tensor=True)
+    return embeddings.tolist()  # Convert tensor to list for Gradio
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_embeddings,
+    inputs=gr.inputs.Textbox(lines=5, placeholder="Enter text chunks here..."),
+    outputs=gr.outputs.JSON(),
+    title="Sentence Transformer Embeddings",
+    description="Generate embeddings for input text chunks."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()