ttt

app.py

@@ -9,12 +9,10 @@ import spaces
 @spaces.GPU
 def get_embeddings(query, task):
     model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral", use_auth_token=os.getenv("HF_TOKEN"))
-    task = "Given a question, retrieve passages that answer the question"
     prompt = f"Instruct: {task}\nQuery: {query}" 
     query_embeddings = model.encode([prompt])  
     return query_embeddings
 
-
 # Initialize a persistent Chroma client and retrieve collection
 client = chromadb.PersistentClient(path="./chroma")
 collection = client.get_collection(name="phil_de")
@@ -88,7 +86,7 @@ def flag_output(query, output_text, output_id):
 max_textboxes = 30
 
 with gr.Blocks(css=".custom-markdown { border: 1px solid #ccc; padding: 10px; border-radius: 5px; }") as demo:
-    gr.Markdown("Enter your query, filter authors (default is all), click **Search** to search. Click **Flag** if a result is relevant to the query and interesting to you. Try reranking the results.")
+    gr.Markdown("Enter your query, filter authors (default is all), click **Search** to search. Click **Flag** if a result is relevant to the query and interesting to you.")
     with gr.Row():
         with gr.Column():
             inp = gr.Textbox(label="query", placeholder="Enter thought...")