Delete admin.py

admin.py

@@ -1,132 +0,0 @@
-import os
-import asyncio
-import logging
-from dotenv import load_dotenv
-from langchain.prompts import PromptTemplate
-from langchain_community.vectorstores import Qdrant
-from langchain.chains import RetrievalQA
-from langchain_groq import ChatGroq
-from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
-from langchain_experimental.text_splitter import SemanticChunker
-from qdrant_client import QdrantClient
-from qdrant_client.http import models as rest
-from langchain_community.document_loaders import PyPDFDirectoryLoader
-import gradio as gr
-
-# Set up logging
-logging.basicConfig(filename='app.log', level=logging.ERROR, format='%(asctime)s %(levelname)s %(message)s')
-load_dotenv()
-
-# Environment variables
-api_key = os.getenv('API_KEY1')
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-qdurl = os.getenv("QDURL")
-
-# Initialize Qdrant Client
-try:
-    client = QdrantClient(
-        url=qdurl,
-        port=6333,
-        verify=False,
-        api_key=api_key,
-    )
-    collections = client.get_collections()
-except Exception as e:
-    print("An error occurred: %s", e)
-    if "server engine not running" in str(e).lower():
-        print("The database engine is not running. Please check the server status.")
-    exit()
-
-print("Database loaded")
-
-# Initialize embeddings and database
-hf = FastEmbedEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5-Q")
-
-db = Qdrant(
-    client=client,
-    embeddings=hf,
-    collection_name="RR2"
-)
-
-load_vector_store = db
-retriever = load_vector_store.as_retriever(search_kwargs={"k":3})
-llm = ChatGroq(temperature=0, model_name="llama3-8b-8192")
-
-# Collection Management Functions
-async def create_collection(url, port, collection_name, vector_size):
-    try:
-        client = QdrantClient(
-            url=url,
-            port=int(port),
-            api_key=api_key,
-            verify=False,
-        )
-        client.recreate_collection(
-            collection_name=collection_name,
-            vectors_config=rest.VectorParams(
-                size=int(vector_size),
-                distance=rest.Distance.COSINE,
-            )
-        )
-        return "Collection created successfully."
-    except Exception as e:
-        return f"Failed to create collection: {str(e)}"
-
-# Data Processing Function
-async def data_ingest_function(data_path, url, collection_name):
-    loop = asyncio.get_event_loop()
-    try:
-        def load_documents():
-            loader = PyPDFDirectoryLoader(data_path)
-            hf = FastEmbedEmbeddings(model_name="nomic-ai/nomic-embed-text-v1.5-Q")
-            text_splitter = SemanticChunker(hf, breakpoint_threshold_type="interquartile")
-            documents = loader.load_and_split(text_splitter=text_splitter)
-            return documents
-
-        texts = await loop.run_in_executor(None, load_documents)
-        print(f"Processed {len(texts)} text chunks")
-
-        def index_documents():
-            qdrant = Qdrant.from_documents(
-                texts,
-                hf,
-                url=url,
-                api_key=api_key,
-                collection_name=collection_name
-            )
-            return qdrant
-
-        await loop.run_in_executor(None, index_documents)
-        return "Data processing and indexing completed successfully."
-    except Exception as e:
-        return f"Failed to process data: {str(e)}"
-
-# Gradio Admin Interface
-with gr.Blocks(theme="soft", title="Admin LLM System", head="Admin for LARGE LANGUAGE MODEL SYSTEM") as admin:
-    with gr.Tab("Collection Management"):
-        with gr.Row():
-            url_input = gr.Textbox(label="Qdrant URL", value="")
-            port_input = gr.Number(label="Port", value=6333)
-            collection_name_input = gr.Textbox(label="Collection Name", value="RR2")
-            vector_size_input = gr.Number(label="Vector Size", value=768)
-            create_collection_btn = gr.Button("Create Collection")
-        create_collection_btn.click(
-            create_collection, 
-            inputs=[url_input, port_input, collection_name_input, vector_size_input], 
-            outputs=gr.Textbox(label="Result")
-        )
-        
-        with gr.Row():
-            data_path_input = gr.Textbox(label="Data Folder Path")
-            url_processing_input = gr.Textbox(label="Qdrant URL for Processing", value="")
-            collection_name_processing_input = gr.Textbox(label="Collection Name for Processing", value="RR2")
-            start_processing_btn = gr.Button("Start Processing")
-        start_processing_btn.click(
-            data_ingest_function, 
-            inputs=[data_path_input, url_processing_input, collection_name_processing_input], 
-            outputs=gr.Textbox(label="Result")
-        )
-
-# Launch Interface
-if __name__ == "__main__":
-    admin.launch(server_name="0.0.0.0", server_port=7860, share=False)