Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModel, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Function to get embeddings
+def get_embedding(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    embeddings = outputs.last_hidden_state.mean(dim=1)
+    return embeddings
+
+st.title("Text Embedding with all-MiniLM-L6-v2")
+st.write("Enter text to get its embedding:")
+
+# Input text from the user
+input_text = st.text_area("Input Text", "")
+
+# If input text is provided, show the embeddings
+if input_text:
+    embedding = get_embedding(input_text)
+    st.write("Embedding:")
+    st.write(embedding.numpy())