csv_result.py

# 1️⃣ Imports
import pandas as pd
from langchain_core.documents import Document
import os
from dotenv import load_dotenv
from functions.data_to_vectors import create_vectorstore
from functions.llm_comm import llm_communication
# --- Load environment variables ---
load_dotenv()
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
def rag(file, question):
    print("📊 Processing CSV File...")
    df = pd.read_csv(file)
    print("Extracted DataFrame:")
    print(df)
    print(f"Chat Input: {question}")
    # Convert each row into a Document
    documents = [
        Document(page_content=row.to_string()) 
        for _, row in df.iterrows()
    ]
    vectorstore = create_vectorstore(str(documents), "csv_store_chroma")

    retrieval_chain = llm_communication(vectorstore)
    # 8️⃣ Query pipeline
    if question:
        response = retrieval_chain.invoke({"input": question})
        print(f"🧠 {question} {response['answer']}\n")
        return response['answer']