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from flask import Flask, request, jsonify
from langchain_community.llms import LlamaCpp
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModel
# cosine_similarity
import torch
from torch.nn.functional import cosine_similarity
import os
app = Flask(__name__)
n_gpu_layers = 0
n_batch = 1024
llm = LlamaCpp(
model_path="Phi-3-mini-4k-instruct-q4.gguf", # path to GGUF file
temperature=0.1,
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
verbose=True,
n_ctx=4096
)
model0 = AutoModel.from_pretrained('sentence-transformers/paraphrase-TinyBERT-L6-v2', force_download=True)
model = SentenceTransformer('sentence-transformers/paraphrase-TinyBERT-L6-v2')
file_size = os.stat('Phi-3-mini-4k-instruct-q4.gguf')
print("model size ====> :", file_size.st_size, "bytes")
@app.route('/cv', methods=['POST'])
def get_skills():
cv_body = request.json.get('cv_body')
# Simple inference example
output = llm(
f"<|user|>\n{cv_body}<|end|>\n<|assistant|>Can you list the skills mentioned in the CV?<|end|>",
max_tokens=256, # Generate up to 256 tokens
stop=["<|end|>"],
echo=True, # Whether to echo the prompt
)
return jsonify({'skills': output})
@app.get('/')
def health():
return jsonify({'status': 'Worked'})
# we will make here post request to compare between lists of skills one has employee just one text and the other has the of jobs has many texts
# the llm will say the most similar job to the cv
@app.route('/compare', methods=['POST'])
def compare():
employee_skills = request.json.get('employee_skills')
jobs_skills = request.json.get('jobs_skills')
if not isinstance(jobs_skills, list) or not all(isinstance(skill, str) for skill in jobs_skills):
raise ValueError("jobs_skills must be a list of strings")
job_embeddings = model.encode(jobs_skills)
employee_embeddings = model.encode(employee_skills)
similarity_scores = []
employee_embeddings_tensor = torch.from_numpy(employee_embeddings).unsqueeze(0)
for i, job_e in enumerate(job_embeddings):
job_e_tensor = torch.from_numpy(job_e).unsqueeze(0)
similarity_score = cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1)
similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.item()})
return jsonify(similarity_scores)
@app.route('/compare_jop', methods=['POST'])
def compare_jop():
employee_skills = request.json.get('post')
jobs_skills = request.json.get('employee_skills')
if not isinstance(employee_skills, list) or not all(isinstance(skill, str) for skill in employee_skills):
raise ValueError("post must be a list of strings")
if not isinstance(jobs_skills, list) or not all(isinstance(skill, str) for skill in jobs_skills):
raise ValueError("employee_skills must be a list of strings")
employee_embeddings = model.encode(employee_skills)
job_embeddings = model.encode(jobs_skills)
similarity_scores = []
employee_embeddings_tensor = torch.from_numpy(employee_embeddings).unsqueeze(0)
for i, job_e in enumerate(job_embeddings):
job_e_tensor = torch.from_numpy(job_e).unsqueeze(0)
similarity_score = cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1)
similarity_scores.append({"job": jobs_skills[i], "similarity_score": similarity_score.item()})
return jsonify(similarity_scores)
if __name__ == '__main__':
app.run() |