Update main.py

main.py

@@ -1,56 +1,44 @@
-import re
-import urllib
-import json
-from fastapi import FastAPI, HTTPException, Query
-from fastapi.middleware.cors import CORSMiddleware
-from transformers import AutoTokenizer, AutoModel
-import torch
-from torch import Tensor
-import torch.nn.functional as F
-import os 
-os.environ['HF_HOME'] = '/'
+import os
+import time
+import shutil
+import subprocess
 
-app = FastAPI()
+# command = ["pip", "install", "flet==0.23.2"]
 
-# Enable CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+# subprocess.run(command, check=True)
 
-model_name = "intfloat/multilingual-e5-large"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModel.from_pretrained(model_name)
+# command = ["pip", "install", "flet-fastapi==0.19.0", "--no-deps"]
 
-def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor:
-    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
-    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+# subprocess.run(command, check=True)
 
-def embed_single_text(text: str) -> Tensor:
-    tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large')
-    model = AutoModel.from_pretrained('intfloat/multilingual-e5-large').cpu()
 
-    batch_dict = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
+gh_token = os.getenv("gh_token")
 
-    with torch.no_grad():
-        outputs = model(**batch_dict)
+url_with_token = f"https://{gh_token}@github.com/Eslam-Magdy-1297/ES-BatchAPI-Backend.git"
 
-    embedding = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+os.system(f"git clone {url_with_token}")
 
-    embedding = F.normalize(embedding, p=2, dim=1)
+time.sleep(10)
 
-    return embedding
+source_dir = "ES-BatchAPI-Backend"
+destination_dir = "."
 
+import os
 
-@app.get("/e5_embeddings")
-def e5_embeddings(query: str = Query(...)):
+# current_directory = os.getcwd()
 
-    result = embed_single_text([query])
+# items = os.listdir(current_directory)
 
-    if result is not None:
-        return result.tolist()
-    else:
-        raise HTTPException(status_code=500)
+# for item in items:
+#     print(item)
+    
+for item in os.listdir(source_dir):
+    s = os.path.join(source_dir, item)
+    d = os.path.join(destination_dir, item)
+    shutil.move(s, d)
+
+os.rmdir(source_dir)
+
+with open("index.py", "r") as file:
+    code = file.read()
+    exec(code)