import uuid
import chromadb
import pandas as pd
import os
from dotenv import load_dotenv
import json
from transformers import AutoModelForCausalLM

load_dotenv()

# ollama_ef = AutoModelForCausalLM.from_pretrained("nomic-embed-text-v1.5.Q5_K_S.gguf",
#     model_type='llama',
#     max_new_tokens = 10960,
#     threads = 3,
# )

csv_files = []
root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
cve_csv_path = os.path.join(root_dir, 'codevulnerabilityai\\data\\cve')

csv_files.extend([os.path.join(cve_csv_path, f) for f in os.listdir(cve_csv_path) if f.endswith('.csv')])

dtype_dict = {
    'Name': str,
    'Status': str,
    'Description': str,
    'References': str,
    'Phase': str,
    'Votes': str,
    'Comments': str
}

chroma_data_path = str(os.getenv('CHROMA_DATA_PATH'))

chroma_db_directory = str("chroma_db/")

client = chromadb.PersistentClient(path=os.path.join(chroma_data_path, chroma_db_directory))

collection = client.get_or_create_collection(name="CVE")

documents_to_add = [] 
ids_to_add = [] 
metadata_to_add = []
documents_to_add_string = []

batch_size = 10
current_batch = 0

if csv_files:
    for csv_file in csv_files:
        print(f"Processing {csv_file}...")
        df = pd.read_csv(csv_file, on_bad_lines='skip', dtype=dtype_dict)

        documents = df['Description'].fillna('').astype(str).tolist()

        if not df.empty and 'Description' in df.columns:
            for index, row in df.iterrows():                
                metadata_parts = row['Name'].split(';')
                metadata = {
                    "Name": str(metadata_parts[0].strip()),
                    "Status": str(metadata_parts[1].strip()) if len(metadata_parts) > 1 else "",
                    "Description": str(metadata_parts[2].strip()) if len(metadata_parts) > 2 else "",
                    "References": str(metadata_parts[3].strip()) if len(metadata_parts) > 3 else "",
                    "Phase": str(metadata_parts[4].strip()) if len(metadata_parts) > 4 else "",
                    "Votes": str(metadata_parts[5].strip()) if len(metadata_parts) > 5 else "",
                }
                document_id = str(uuid.uuid4())
                
                document_content = metadata["Description"]

                document = {'id': document_id, 'content': document_content}
                
                documents_to_add.append(document)
                documents_to_add_string.append(json.dumps(documents_to_add)) 
                ids_to_add.append(document_id)
                metadata_to_add.append(metadata)

                current_batch += 1
                if current_batch % batch_size == 0:
                    print(f"Batch {current_batch // batch_size} added to the collection.")
                    collection.add(documents=documents_to_add_string, ids=ids_to_add, metadatas=metadata_to_add)
                    documents_to_add = [] 
                    ids_to_add = [] 
                    metadata_to_add = []
                    documents_to_add_string = []
                    print(f"Batch {current_batch // batch_size} completed.")

        else:
            print(f"Skipping file {csv_file} due to empty DataFrame or missing 'Description' column")
else:
    print("No CSV files found in the directory. Skipping processing.")

# Add the remaining documents if there are less than 100 left
if documents_to_add:
    print(f"Adding remaining {len(documents_to_add)} documents to the collection.")
    collection.add(documents=documents_to_add_string, ids=ids_to_add, metadatas=metadata_to_add)

# results = collection.query(
#     query_texts=["Dotnet"],
#     n_results=3,
# )

# print(results)