remove duplicate code

app.py

@@ -1,103 +1,3 @@
-# main.py
-import spaces
-import torch
-import torch.nn.functional as F
-from torch.nn import DataParallel
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModel
-import threading
-import queue
-import os
-import json
-import numpy as np
-import gradio as gr
-from huggingface_hub import InferenceClient
-import openai
-from openai import OpenAI
-from globalvars import API_BASE, intention_prompt, tasks , system_message, model_name
-from dotenv import load_dotenv
-import re 
-from utils import load_env_variables
-import chromadb
-from chromadb import Documents, EmbeddingFunction, Embeddings  
-from chromadb.config import Settings  
-from chromadb import HttpClient  
-from langchain_community.document_loaders import UnstructuredFileLoader  
-from utils import load_env_variables  , parse_and_route
-
-os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:30'
-os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
-os.environ['CUDA_CACHE_DISABLE'] = '1'
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-hf_token, yi_token = load_env_variables()
-
-def clear_cuda_cache():
-    torch.cuda.empty_cache()
-
-client = OpenAI(
-    api_key=yi_token,
-    base_url=API_BASE
-)
-
-
-class EmbeddingGenerator:  
-    def __init__(self, model_name: str, token: str, intention_client):  
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=token, trust_remote_code=True)  
-        self.model = AutoModel.from_pretrained(model_name, token=token, trust_remote_code=True).to(self.device)  
-        self.intention_client = intention_client  
-  
-    def clear_cuda_cache(self):  
-        torch.cuda.empty_cache()  
-    
-    @spaces.GPU
-    def compute_embeddings(self, input_text: str):  
-        # Get the intention  
-        intention_completion = self.intention_client.chat.completions.create(  
-            model="yi-large",  
-            messages=[  
-                {"role": "system", "content": intention_prompt},  
-                {"role": "user", "content": input_text}  
-            ]  
-        )  
-        intention_output = intention_completion.choices[0].message['content']  
-  
-        # Parse and route the intention  
-        parsed_task = parse_and_route(intention_output)  
-        selected_task = list(parsed_task.keys())[0]  
-  
-        # Construct the prompt  
-        try:  
-            task_description = tasks[selected_task]  
-        except KeyError:  
-            print(f"Selected task not found: {selected_task}")  
-            return f"Error: Task '{selected_task}' not found. Please select a valid task."  
-          
-        query_prefix = f"Instruct: {task_description}\nQuery: "  
-        queries = [input_text]  
-  
-        # Get the embeddings  
-        with torch.no_grad():  
-            inputs = self.tokenizer(queries, return_tensors='pt', padding=True, truncation=True, max_length=4096).to(self.device)  
-            outputs = self.model(**inputs)  
-            query_embeddings = outputs.last_hidden_state.mean(dim=1)  
-          
-        # Normalize embeddings  
-        query_embeddings = F.normalize(query_embeddings, p=2, dim=1)  
-        embeddings_list = query_embeddings.detach().cpu().numpy().tolist()  
-        self.clear_cuda_cache()  
-        return embeddings_list  
-  
-  
-class MyEmbeddingFunction(EmbeddingFunction):  
-    def __init__(self, embedding_generator: EmbeddingGenerator):  
-        self.embedding_generator = embedding_generator  
-  
-    def __call__(self, input: Documents) -> Embeddings:  
-        embeddings = [self.embedding_generator.compute_embeddings(doc) for doc in input]  
-        embeddings = [item for sublist in embeddings for item in sublist]  
-        return embeddings  
 # main.py  
 import os  
 import uuid