Llama_Utility.py

import re
import os
from datetime import datetime
import openai
from google.cloud import firestore
from dotenv import load_dotenv
import pandas as pd
import ast
import json
from pandasai.responses.response_parser import  ResponseParser
# Load environment variables and configure Firestore
load_dotenv()
db = firestore.Client.from_service_account_json("firestore-key.json")
from langchain_google_genai import ChatGoogleGenerativeAI
# Initialize the ChatSambaNovaCloud LLM client (using the latest model)
from langchain_community.chat_models.sambanova import ChatSambaNovaCloud
import google.generativeai as genai

llm = ChatSambaNovaCloud(
    model="Meta-Llama-3.1-70B-Instruct",
    max_tokens=1024,
    temperature=0.7,
    top_k=1,
    top_p=0.01,
)



# Response parser for formatting outputs from pandasai
class FlaskResponse(ResponseParser):
  def __init__(self, context) -> None:
      super().__init__(context)

  def format_dataframe(self, result):
      return result['value'].to_html()

  def format_plot(self, result):
        # Save the plot using savefig
      try:
          
          img_path = result['value']
      
        
      except ValueError:
          img_path = str(result['value'])
          print("value error!", img_path)
        
      print("response_class_path:", img_path)
      return img_path

  def format_other(self, result):
      return str(result['value'])
      
def generateResponse(prompt, model='Meta-Llama-3.1-70B-Instruct'):
    # Templates for extracting transaction information
    relevant_info_template = """
Intent: The CRUD operation (create, read, update, delete)
Transaction Type: e.g. Purchase, Sale, Inventory
Details: A list of key detail fields extracted.
"""
    sample_single_transaction_template = """
*Intent*: Create
*Transaction Type*: Purchase
*Details*: - Item: Car, - Quantity: 1, - Cost: 10000, etc.
"""
    sample_multi_transaction_template = """
*Intent*: Create
Transaction 1:
*Transaction Type*: Purchase
*Details*: - Item: Car, - Quantity: 1, etc.
Transaction 2:
*Transaction Type*: Sale
*Details*: - Item: Chair, - Quantity: 2, etc.
"""
    response = openai.OpenAI(
        api_key=os.environ.get("SAMBANOVA_API_KEY"),
        base_url="https://api.sambanova.ai/v1",
    ).chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": 
             f"You are a helpful assistant that classifies transactions. Format your output with these guidelines: {relevant_info_template} "
             f"Sample single transaction: {sample_single_transaction_template} "
             f"Sample multi-transaction: {sample_multi_transaction_template}"},
            {"role": "user", "content": prompt}
        ]
    )
    try:
        response_text = response.choices[0].message.content
    except Exception as e:
        print(f'An error occurred: {str(e)}')
        response_text = None
    return response_text

def parse_value(value):
    value = value.strip()
    try:
        # Try to detect currency symbols or codes
        currency_match = re.search(r"([A-Z]{3}|\$|€|£)", value)
        currency = currency_match.group(1) if currency_match else None

        # Remove currency symbols/codes for numeric conversion
        cleaned_value = re.sub(r"([A-Z]{3}|\$|€|£)", "", value).replace(",", "").strip()

        if "%" in cleaned_value:
            return float(cleaned_value.replace("%", "")), currency
        elif cleaned_value.replace(".", "", 1).isdigit():
            return float(cleaned_value) if "." in cleaned_value else int(cleaned_value), currency
        return value, currency
    except ValueError:
        return value, None

def extract_transaction_details(text):
    details = {}
    transaction_currency = None  # Default currency
    # Use regex to extract key: value pairs (handles bold formatting if present)
    detail_matches = re.findall(
        r"-\s*\*{0,2}([\w\s]+)\*{0,2}:\s*([\w\s,.$%-]+?)(?:\s*[\n]|$)",
        text,
        re.DOTALL
    )
    for field, value in detail_matches:
        field_key = field.strip().lower().replace(" ", "_")
        parsed_value, detected_currency = parse_value(value)
        if detected_currency and not transaction_currency:
            transaction_currency = detected_currency
        details[field_key] = parsed_value
    if transaction_currency:
        details["currency"] = transaction_currency
    return details

def parse_ai_response(response_text):
    data = {
        "intent": None,
        "transaction_type": None,
        "details": {},
        "created_at": datetime.now().isoformat()
    }
    intent_match = re.search(r"\*Intent\*:\s*(\w+)", response_text)
    if intent_match:
        data["intent"] = intent_match.group(1)
    transaction_type_match = re.search(r"\*Transaction Type\*:\s*(\w+)", response_text)
    if transaction_type_match:
        data["transaction_type"] = transaction_type_match.group(1)
    data["details"] = extract_transaction_details(response_text)
    return data

def parse_multiple_transactions(response_text):
    transactions = []
    # Split the response into sections by "Transaction <number>:"
    transaction_sections = re.split(r"Transaction \d+:", response_text, flags=re.IGNORECASE)
    transaction_sections = [section.strip() for section in transaction_sections if section.strip()]
    # If the first section does not contain transaction info, remove it
    if transaction_sections and not re.search(r"\*Transaction Type\*", transaction_sections[0], re.IGNORECASE):
        transaction_sections.pop(0)
    intent_match = re.search(r"\*Intent\*:\s*(\w+)", response_text)
    intent = intent_match.group(1) if intent_match else None
    for section in transaction_sections:
        data = {
            "intent": intent,
            "transaction_type": None,
            "details": {},
            "created_at": datetime.now().isoformat()
        }
        transaction_type_match = re.search(r"\*Transaction Type\*:\s*(\w+)", section)
        if transaction_type_match:
            data["transaction_type"] = transaction_type_match.group(1)
        data["details"] = extract_transaction_details(section)
        transactions.append(data)
    return transactions

def read_datalake(user_phone, user_question):
    inventory_ref = db.collection("users").document(user_phone).collection("inventory")
    sales_ref = db.collection("users").document(user_phone).collection("sales")
    inventory_list = [doc.to_dict() for doc in inventory_ref.stream()]
    sales_list = [doc.to_dict() for doc in sales_ref.stream()]
    inventory_df = pd.DataFrame(inventory_list)
    sales_df = pd.DataFrame(sales_list)
    from pandasai import SmartDatalake
    lake = SmartDatalake(
        [inventory_df, sales_df],
        config={
            "llm": llm,
            "custom_whitelisted_dependencies": ["ast"],
            "response_parser": FlaskResponse,
            "enable_cache": False,
            "save_logs": False
        }
    )
    response = lake.chat(user_question)
    return response

def create_inventory(user_phone, transaction_data):
    for transaction in transaction_data:
        item_name = transaction['details'].get('item')
        if item_name:
            doc_ref = db.collection("users").document(user_phone).collection("inventory").document(item_name)
            doc_ref.set(transaction)
    return True

def create_sale(user_phone, transaction_data):
    for transaction in transaction_data:
        item_name = transaction['details'].get('item')
        if not item_name:
            continue
        inventory = fetch_transaction(user_phone, item_name)
        if inventory and inventory['details'].get('quantity') is not None:
            new_stock = inventory['details'].get('quantity') - transaction['details'].get('quantity', 0)
            inventory['details']['quantity'] = new_stock
            inv_ref = db.collection("users").document(user_phone).collection("inventory").document(item_name)
            inv_ref.set(inventory)
            sale_ref = db.collection("users").document(user_phone).collection("sales").document(item_name)
            sale_ref.set(transaction)
    return True

def update_transaction(user_phone, transaction_id, update_data):
    doc_ref = db.collection("users").document(user_phone).collection("transactions").document(transaction_id)
    doc_ref.update(update_data)
    return True

def fetch_transaction(user_phone, transaction_id=None):
    if transaction_id:
        doc_ref = db.collection("users").document(user_phone).collection("inventory").document(transaction_id)
        transaction = doc_ref.get()
        if transaction.exists:
            return transaction.to_dict()
        return None
    else:
        collection_ref = db.collection("users").document(user_phone).collection("inventory")
        transactions = [doc.to_dict() for doc in collection_ref.stream()]
        return transactions

def delete_transaction(user_phone, transaction_data):
    # Assume transaction id defaults to the item name in transaction details
    transaction_id = transaction_data[0]['details'].get('item')
    transaction_type = transaction_data[0]['transaction_type']
    if transaction_type:
        transaction_type = transaction_type.lower()
    else:
        transaction_type = "inventory"
    doc_ref = db.collection("users").document(user_phone).collection(transaction_type).document(transaction_id)
    item_doc = doc_ref.get()
    if item_doc.exists:
        doc_ref.delete()
        return True
    else:
        return False

def persist_temporary_transaction(transactions, mobile):
    temp_ref = db.collection("users").document(mobile).collection("temp_transactions").document('pending-user-action')
    data = {
        "transactions": transactions,
        "status": "pending",
        "created_at": datetime.now().isoformat()
    }
    temp_ref.set(data)
    return True

def process_intent(parsed_trans_data, mobile):
    """
    Process the detected intent and handle transactions accordingly.
    - For 'create', it differentiates between purchase/inventory and sale.
    - For 'delete', it attempts to delete the transaction.
    - Returns a message indicating the result.
    """
    intent = parsed_trans_data[0]['intent'].lower() if parsed_trans_data and parsed_trans_data[0].get('intent') else None
    trans_type = parsed_trans_data[0]['transaction_type'].lower() if parsed_trans_data and parsed_trans_data[0].get('transaction_type') else None

    if intent == 'create':
        if trans_type in ('purchase', 'purchases', 'inventory'):
            if create_inventory(mobile, parsed_trans_data):
                firestore_msg = "Transaction recorded successfully!"
            else:
                firestore_msg = "Sorry, could not record transaction!"
        elif trans_type in ('sale', 'sales'):
            if create_sale(mobile, parsed_trans_data):
                firestore_msg = "Transaction recorded successfully!"
            else:
                firestore_msg = "Sorry, could not record transaction!"
        else:
            firestore_msg = f"Transaction type '{trans_type}' is not supported for create operations."
    elif intent == 'update':
        # Update logic would be implemented here.
        firestore_msg = "Update operation not implemented yet."
    elif intent == 'delete':
        item = parsed_trans_data[0]['details'].get('item', 'item')
        item_deleted = delete_transaction(mobile, parsed_trans_data)
        if item_deleted:
            firestore_msg = f"You successfully deleted {item} from {trans_type}!"
        else:
            firestore_msg = f"Sorry, could not delete {item} from {trans_type}!"
    else:
        firestore_msg = f"The detected intent, {intent}, is not currently supported!"
    return firestore_msg