Update app.py

app.py

@@ -1,85 +1,26 @@
-import gradio as gr
-from fastapi import FastAPI, Request
-import uvicorn
-from sentence_transformers import SentenceTransformer
-from sentence_transformers.util import cos_sim
-from sentence_transformers.quantization import quantize_embeddings
-
-
-import spaces
-
-
-
-app = FastAPI()
-
-
-@spaces.GPU
-def embed(text):
-    return [0,1]
-    #query_embedding = Embedder.encode(text)
-    #return query_embedding.tolist();
-    
-    
-    
-@app.post("/v1/embeddings")
-async def openai_embeddings(request: Request):
-    body = await request.json();
-    print(body);
-    
-    model = body['model']
-    text = body['input'];
-    embeddings = embed(text)
-    return {
-		'object': "list"
-		,'data': [{
-			'object': "embeddings"
-			,'embedding': embeddings
-			,'index':0
-		}]
-		,'model':model
-		,'usage':{
-			 'prompt_tokens': 0
-			,'total_tokens': 0
-		}
-	}
-
-def fn(text):
-    return embed(text);
-
-with gr.Blocks(fill_height=True) as demo:
-    text = gr.Textbox();
-    embeddings = gr.Textbox()
-    
-    text.submit(fn, [text], [embeddings]);
-    
-
-print("Loading embedding model");
-Embedder = None #SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
-
-# demo.run_startup_events() 
- 
- 
-#demo.launch(
-#    share=False,
-#    debug=False,
-#    server_port=7860,
-#    server_name="0.0.0.0",
-#    allowed_paths=[]
-#)
-
-print("Demo run...");
-(app2,url,other) = demo.launch(prevent_thread_lock=True, server_name=None, server_port=8000);
-
-print("Mounting app...");  
-GradioApp = gr.mount_gradio_app(app, demo, path="/", ssr_mode=False); 
-
-
-demo.close();
-
-if __name__ == '__main__':
-    print("Running uviconr...");
-    uvicorn.run(GradioApp, host="0.0.0.0", port=7860)
-
-
-
-    
+import gradio as gr
+from fastapi import FastAPI, Request
+import uvicorn
+import spaces
+
+app = FastAPI()
+
+@spaces.GPU
+def embed(text):
+  # my embedding logic here (e.g: sentence transformers)
+  pass
+
+@app.post("/v1/embeddings")
+def openai_embed(req:Request):
+  # some logic that will call embed
+  embed("some data from request")
+
+
+with gr.Blocks() as demo:
+    text = gr.Textbox();
+    embeddings = gr.Textbox()
+    
+    text.submit(embed, [text], [embeddings]);
+
+GradioApp = gr.mount_gradio_app(app, demo, path="/");
+uvicorn.run(GradioApp, port=7860, host="0.0.0.0")