from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Cargar el modelo y tokenizer de LLaMA 2
model_name = "meta-llama/Llama-2-7b-hf"  # O ajusta según el modelo que prefieras
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Crear prompt
prompt = """
You are an advanced AI assistant trained to process job titles and user queries. I will provide you with a list of job titles, and a user query. Your task is to:

1. Calculate the cosine similarity score between the query and each job title.
2. Rank the job titles from the most similar to the least similar based on their semantic meaning.
3. Return the top 5 job titles with their cosine similarity scores.

Here is the list of job titles from the CSV:
- Software Engineer
- Data Scientist
- Machine Learning Engineer
- Business Analyst
- Product Manager
...

The user's query is: "Machine Learning Expert"

Now, compute the similarity scores, rank the job titles, and return the top 5.
"""

# Tokenizar y generar respuesta
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)

# Decodificar y mostrar resultados
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)