Update app.py

app.py

@@ -6,6 +6,7 @@ from langchain.llms import HuggingFacePipeline
 from huggingface_hub import login
 from pydantic import BaseModel, model_validator
 
+
 # Token Secret of Hugging Face
 huggingface_token = st.secrets["HUGGINGFACEHUB_API_TOKEN"]
 login(huggingface_token)
@@ -50,18 +51,19 @@ if uploaded_file is not None:
         if query:
             st.write("Query:", query)
 
-            # Crear el prompt para el LLM
             prompt = f"""
-            You are an AI assistant. You will be given a list of job titles and a search query. 
-            Your task is to calculate the cosine similarity score between the query and each job title.
-            You should then return a sorted list of job titles based on the similarity score, from highest to lowest.
-            Provide the output in the following format:
-
-            1. Job Title: [Job Title], Score: [Cosine Similarity Score]
-            2. Job Title: [Job Title], Score: [Cosine Similarity Score]
+            You are an AI assistant. You have a list of job titles and a search query.
+            Your task is to rank these job titles by their semantic similarity to the given query. 
+            Please provide the ranking from most relevant to least relevant. 
+            Do not calculate cosine similarity; instead, focus on understanding the semantic relevance of each job title to the query.
+            
+            Format your response like this:
+            1. [Most Relevant Job Title]
+            2. [Second Most Relevant Job Title]
             ...
+            N. [Least Relevant Job Title]
             
-            Query: {query}
+            Query: "{query}"
             Job Titles: {job_titles}
             """