Create app.py

app.py

@@ -0,0 +1,34 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Load the model and tokenizer
+@st.cache(allow_output_mutation=True)
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("Salesforce/SFR-Embedding-Mistral")
+    model = AutoModel.from_pretrained("Salesforce/SFR-Embedding-Mistral")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+def embed_text(text):
+    inputs = tokenizer(text, return_tensors='pt', truncation=True, max_length=32768)
+    outputs = model(**inputs)
+    return outputs.last_hidden_state.mean(dim=1).detach().numpy()
+
+def main():
+    st.title("Text Embedding using Salesforce/SFR-Embedding-Mistral")
+
+    # Text input
+    text = st.text_area("Enter text here:", height=150)
+    
+    if st.button("Get Embeddings"):
+        if text:
+            with st.spinner('Fetching embeddings...'):
+                embeddings = embed_text(text)
+                st.write(embeddings)
+        else:
+            st.warning("Please enter some text to process.")
+
+if __name__ == "__main__":
+    main()