Delete main.py

main.py

@@ -1,87 +0,0 @@
-from flask import Flask, request, jsonify
-from langchain_community.llms import LlamaCpp
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer, AutoModel
-from huggingface_hub import hf_hub_download, HfApi
-
-# cosine_similarity
-import torch
-from torch.nn.functional import cosine_similarity
-import os
-
-app = Flask(__name__)
-
-n_gpu_layers = 0
-n_batch = 1024
-
-# تنزيل النموذج باستخدام معالجة الأخطاء
-try:
-    model_path = hf_hub_download(repo_id="repo_name", filename="model_file_name", force_download=True)
-except Exception as e:
-    print(f"Error downloading the model: {e}")
-    model_path = None
-
-
-if model_path:
-    llm = LlamaCpp(
-        model_path=model_path,  # 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')
-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"\n{cv_body}\nCan you list the skills mentioned in the CV?",
-        max_tokens=256,  # Generate up to 256 tokens
-        stop=[""], 
-        echo=True,  # Whether to echo the prompt
-    )
-
-    return jsonify({'skills': output})
-
-@app.get('/')
-def health():
-    return jsonify({'status': 'Worked'})
-
-@app.route('/compare', methods=['POST'])
-def compare():
-    employee_skills = request.json.get('employee_skills')  # string
-    jobs_skills = request.json.get('jobs_skills')  # list of strings
-
-    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)
-    sim = []
-    employee_embeddings_tensor = torch.from_numpy(employee_embeddings).unsqueeze(0)
-    for job_e in job_embeddings:
-        job_e_tensor = torch.from_numpy(job_e).unsqueeze(0)
-        sim.append(cosine_similarity(employee_embeddings_tensor, job_e_tensor, dim=1))
-
-    max_sim = max(sim)
-    index = sim.index(max_sim)
-    return jsonify({'job': jobs_skills[index]})
-
-@app.route('/models', methods=['GET'])
-def list_models():
-    hf_api = HfApi()
-    models = hf_api.list_models()
-    return jsonify({'models': models})
-
-if __name__ == '__main__':
-    app.run()
-