Update app.py

app.py

@@ -1,34 +1,3 @@
-import streamlit as st
-from transformers import AutoTokenizer, AutoModel
-import torch
+import gradio as gr
 
-# 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()
+gr.load("models/Salesforce/SFR-Embedding-Mistral").launch()